CKD-EPI is the most reliable equation to estimate renal function in patients with systemic lupus erythematosus
La ecuación CKD-EPI es la más fiable para estimar la función renal en pacientes con lupus eritematoso sistémico
Marco U. Martínez-Martíneza, Marco Ulises Martínez-Martínezb, Peter Mandevillec, Lilia Llamazares-Azuarad, Lilia Llamazares-Azuarae, Carlos Abud-Mendozab
a Regional Unit of Rheumatology and Osteoporosis, Hospital Central Dr. Ignacio Morones Prieto and Faculty of Medicine, Universidad Autónoma de San Luis Potosí, San Luis Potosí, México,
b Regional Unit of Rheumatology and Osteoporosis, Hospital Central¿Dr. Ignacio Morones Prieto¿ and Faculty of Medicine, Universidad Autónoma de San Luis Potosí, San Luis Potosí, San Luis Potosí, México,
c Statistics and Epidemiology, Universidad Autónoma de San Luis Potosí, San Luis Potosí, San Luis Potosí, México,
d Department of Nephrology and Renal Diseases, Universidad Autónoma de San Luis Potosí, San Luis Potosí, San Luis Potosí, México,
e Department of Nephrology and Renal Diseases, Universidad Autónoma de San Luis Potosí, San Luis Potosí, México,
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For example, the MDRD study equation performs well in patients with chronic kidney disease, but is less accurate in potential kidney donors, young people with type-1 diabetes, and patients with substantially reduced muscle mass;<span class="elsevierStyleSup">9-11</span> moreover, CG is an estimate of CrCl originally developed in a predominantly male, Caucasian population.<span class="elsevierStyleSup">12</span> Conclusions of these and other studies about equations may not be appropriate in SLE, a predominantly female disease,<span class="elsevierStyleSup">13</span> encompassing many ethnicities,<span class="elsevierStyleSup">13</span> reduced muscle mass,<span class="elsevierStyleSup">14</span> and with a wide range of renal function.<span class="elsevierStyleSup">15</span> However, there is a paucity of data regarding the equations for estimating renal function in patients with SLE.<span class="elsevierStyleSup">15-17</span> Moreover, almost half of the rheumatologists use CrCl in all of their patients in order to estimate GFR, a method with a high rate of inappropriate collection and more expensive than the creatinine based equations.<span class="elsevierStyleSup">18</span></p><p class="elsevierStylePara">To our knowledge, no study has determined the best equation for estimating renal function in patients with SLE, starting from iothalamate urinary clearance as gold standard. Therefore, we performed this study to evaluate which is the best equation, based on serum creatinine, to assess renal function in patients with SLE.</p><p class="elsevierStylePara"> </p><p class="elsevierStylePara"><span class="elsevierStyleBold">PATIENTS AND METHODS</span></p><p class="elsevierStylePara"> </p><p class="elsevierStylePara">We included, in consecutive selection, all patients with SLE according to the American College of Rheumatology (ACR) criteria.<span class="elsevierStyleSup">19,20</span></p><p class="elsevierStylePara">Exclusion criteria: patients under 18 years old, asthma, hypothyroidism, current smoking, malignancy, prednisone doses ≥30mg/d (or the equivalent dose of another glucocorticoid), pregnancy and severe disease activity (MEX-SLEDAI≥8).<span class="elsevierStyleSup">21</span> All patients signed informed consent and our study was approved by the Central Hospital Institutional Review Board.</p><p class="elsevierStylePara">Ideal body weight (IBW) was calculated (Robinson equation).<span class="elsevierStyleSup">22</span> Body surface index (BSI) was calculated in accordance with the Dubois equation.<span class="elsevierStyleSup">23</span></p><p class="elsevierStylePara">We divided this study into 2 phases.</p><p class="elsevierStylePara"><span class="elsevierStyleBold">Phase 1: </span>We measured GFR with iothalamate urinary clearance in 14 patients with SLE. GFR calculated through iothalamate was compared with the GFR estimated through cystatin and/or creatinine based equations. The main objective in this phase was to validate the best cystatin/creatinine-based equation to use at the second phase as our reference standard (Table 1).<span class="elsevierStyleSup">11,24-27</span> We selected cystatin-based equations for this study from equations developed in other studies taking into account: studies were developed from adult patients, with more than 100 patients, and the measurement of cystatin C had been made by the same method used in our study (immunonephelometry, PENIA).</p><p class="elsevierStylePara"><span class="elsevierStyleBold">Phase 2:</span> The best cystatin/creatinine-based equation found in phase I was taken as the reference standard (measured GFR) to assess CrCl and creatinine-based equations (Table 1) in 55 patients (estimated GFR).<span class="elsevierStyleSup">5,7,28</span></p><p class="elsevierStylePara"> </p><p class="elsevierStylePara"><span class="elsevierStyleBold">Laboratory tests</span></p><p class="elsevierStylePara"> </p><p class="elsevierStylePara">We recalibrated serum creatinine values to standardized creatinine measurements by using the Roche enzymatic method as described elsewhere.<span class="elsevierStyleSup">10,29</span></p><p class="elsevierStylePara">Serum cystatin C was measured using a particle-enhanced immunonephelometric assay (N Latex Cystatin C, Dade Behring, IL). The between-day assay coefficient of variation was 3.5%.</p><p class="elsevierStylePara">In phase 1, all patients had a non-radiolabeled iothalamate clearance using a previously described standard laboratory method.<span class="elsevierStyleSup">30</span> In brief, after oral hydration with 4–6 glasses of water, patients received a subcutaneous injection of non-radiolabeled iothalamate (Conray<span class="elsevierStyleSup">®</span>). Following a 1-h equilibrium period, the patient voided, the first serum sample was drawn and a timed urine collection was begun. A sonographic scanner assessed bladder emptying and a bladder catheter was placed in patients with urinary retention. Following the timed urine collection (approximately 45–60min), a second serum sample was obtained. GFR was calculated by the clearance equation (UIoV/Pio where UIo and PIo are the urine and plasma concentrations of iothalamate, and V is the urine flow) using the mean of two serum samples and one urine sample assayed for iothalamate via capillary electrophoresis. All GFR measurements were standardized for BSI (per 1.73m<span class="elsevierStyleSup">2</span>) by multiplying by 1.73 and dividing by BSI.</p><p class="elsevierStylePara"> </p><p class="elsevierStylePara"><span class="elsevierStyleBold">Statistical analysis</span></p><p class="elsevierStylePara"> </p><p class="elsevierStylePara">We used R, version 2 (Free Software Foundation, Boston, Massachusetts) to compute all analyses.</p><p class="elsevierStylePara">Categorical variables are presented as frequencies and percentages, and continuous variables as means and standard deviations, or medians and (minimum-maximum) for variables with skewed distributions.</p><p class="elsevierStylePara">Measured GFR (mGFR) and estimated GFR (eGFR) were compared for each patient graphically by plotting mGFR and the difference (mGFR - eGFR) against eGFR. A smoothed regression line is shown with the 95% confidence interval (95% CI) computed by using the lowest smoothing function in R, and using quantile regression, excluding the lowest and highest 2.5% of estimated GFR. Bias was expressed as the median difference (mGFR - eGFR), with positive values indicating an under-estimation of mGFR. Precision was expressed as interquartile range (IQR) for the differences (dIQR). Accuracy was expressed as the percentage of eGFR within 30% of mGFR (P30). Confidence intervals (CI) were computed by using bootstrap methods (2,000 bootstraps) for median differences and IQR of the differences and by the binomial method for P30.</p><p class="elsevierStylePara">The reference method or mGFR was iothalamate for phase I, and the best cystatin/creatinine-based equation (Stevens’ equation) for phase 2.</p><p class="elsevierStylePara">The best equation in both phases was that which had less bias, less dIQR and higher P30.</p><p class="elsevierStylePara">In phase 2 we constructed a 2x2 contingency table to determine the sensitivity and specificity of each equation to classify if each one of the patients had or not had GFR<60 ml/min per 1.73m<span class="elsevierStyleSup">2</span>.</p><p class="elsevierStylePara"> </p><p class="elsevierStylePara"><span class="elsevierStyleBold">RESULTS </span></p><p class="elsevierStylePara"> </p><p class="elsevierStylePara">In the first phase we included 14 patients; their median age was 32.5 years old, and 61.5 months of mean SLE evolution time (Table 2). Table 3 shows the results of the first phase which highlights the fact that the equation based on creatinine and cystatin C developed by Stevens and colleagues<span class="elsevierStyleSup">11</span> (Stevens’ equation) had the best bias and the most accuracy.</p><p class="elsevierStylePara">Because our patients were selected for their low disease activity and drug doses, and because of the interference of these factors may have on cystatin C levels, for the clinician, the better setting is to have a creatinine-based equation than a cystatin-based equation, so we performed a second phase with more patients to determine the best creatinine-based equation to estimate GFR in patients with SLE (Table 1).</p><p class="elsevierStylePara">Table 4 shows how the bias, precision and accuracy are better with the CKD-EPI equation than with the others. In this phase, 55 patients were included; median age was 36 years old and SLE evolution time was 60 months (Table 2).</p><p class="elsevierStylePara">Figure 1 shows the difference between measured and estimated versus estimated GFR in phase 2. A smoothed regression line is shown with the 95% CI, using quantile regression. Notice how the CKD-EPI equation has a good performance both at high and low GFR.</p><p class="elsevierStylePara">Table 5 shows sensitivity and specificity of each equations to detect GFR less than 60ml/min/1.73. Eleven patients had GFR less than 60ml/min/1.73 according to Stevens’ equation, CKD-EPI misclassified 1 patient (1.8%), sMDRD 2 (3.6%), CG 4 (7.3%), CGi 6 (10.9%), MCQ 5 (9.1) and CrCl 4 (7.3%).</p><p class="elsevierStylePara"> </p><p class="elsevierStylePara"><span class="elsevierStyleBold">DISCUSSION</span></p><p class="elsevierStylePara"> </p><p class="elsevierStylePara">The GFR should be determined as accurately as possible, ideally with an accessible and inexpensive method which does not cause harm to the patient. Several factors affect serum creatinine level other than GFR, including its generation from muscle metabolism; these factors compromise the generalization of the equations in patients with SLE. Cystatin-based equations has no dependence on muscle mass, but previous studies showed evidence for non-GFR determinants of cystatin C (inflammation, glucocorticoids, thyroid disease and cytotoxic drugs); variation in these factors can restrict the use of this protein for measuring GFR in patients with SLE.<span class="elsevierStyleSup">31</span> Even though we reported previously that Cystatin C does not correlate with disease activity and drug doses,<span class="elsevierStyleSup">32</span> we selected our patients avoiding these factors.</p><p class="elsevierStylePara">Because there are multiple equations to estimate GFR, the management guidelines of patients with SLE are based on studies in other different diseases from SLE and we think that the results on these diseases cannot be extrapolated to SLE.<span class="elsevierStyleSup">8</span> National Kidney Foundation (NKF) guidelines suggest using the GFR estimating equations (not the CrCl) to achieve a better assessment of renal function.<span class="elsevierStyleSup">6,7</span> In spite of these recommendations, 24 h urine collection to determine CrCl is a widely used method for assessing GRF in patients with SLE (including clinical trials).<span class="elsevierStyleSup">16,33-35</span></p><p class="elsevierStylePara">Our study identifies several important findings: 1) The equation developed by Stevens and colleagues based on creatinine and cystatin C is the best equation to estimate renal function for our specifically selected SLE patients (with low disease activity, low doses of glucocorticoids, no smoking, no hypothyroidism). 2) Because of all those factors that the clinicians must take into account when using cystatin C in a patient with SLE (smoking, hypothyroidism, etc.), we looked for an equation based only on serum creatinine in which there is not the influence of these factors and our results were: the CKD-EPI equation should be used in patients with SLE.</p><p class="elsevierStylePara">The importance of our results is that we do not need a 24h urine collection to evaluate renal function in SLE patients as was done in several clinical trials.<span class="elsevierStyleSup">33-35</span> Moreover, CKD-EPI is more reliable than CrCl and the other equations, as our study describes. Rheumatologists can estimate renal function easily with the demographic data, standardized creatinine and with the help of internet or a smart phone (using CKD-EPI).</p><p class="elsevierStylePara">There are several limitations to this analysis. First, the study population was composed only of Mexican patients; however, the main objective of the current work was to find the best available creatinine-based equation, and, in this way, our results can be generalized. Finally, these equations were not tested for assessment of change in GFR over time, but this is a diagnostic study in which we wanted to clarify the best equation and not the change over time.</p><p class="elsevierStylePara">With our results, we suggest that the initial classification of patients with SLE should be with CKD-EPI, because of its reproducibility, ability to predict GFR in patients with low or high GFR, and ease of performance.</p><p class="elsevierStylePara"><span class="elsevierStyleBold"> </span></p><p class="elsevierStylePara"><span class="elsevierStyleBold">Acknowledgements</span></p><p class="elsevierStylePara"><span class="elsevierStyleBold"> </span></p><p class="elsevierStylePara">We are very grateful to Mr. John W Covey for his suggestions in manuscript preparation, and with Juan Ramón Torres Anguiano and Carlos Alberto Hernández Nieto for their participation in selection of patients. </p><p class="elsevierStylePara"> </p><p class="elsevierStylePara"><span class="elsevierStyleBold">Conflict of interest</span></p><p class="elsevierStylePara"> </p><p class="elsevierStylePara">The authors declare that there is no conflict of interest associated with this manuscript.</p><p class="elsevierStylePara"><a href="grande/11101_16025_19898_en_f111101i6_copia.jpg" class="elsevierStyleCrossRefs"><img src="11101_16025_19898_en_f111101i6_copia.jpg" alt="Difference between measured and estimated versus estimated GFR"></img></a></p><p class="elsevierStylePara">Figure 1. Difference between measured and estimated versus estimated GFR</p><p class="elsevierStylePara"><a href="grande/11101_16025_39618_en_t111101i2_copia.jpg" class="elsevierStyleCrossRefs"><img src="11101_16025_39618_en_t111101i2_copia.jpg" alt="Equations for estimating GFR"></img></a></p><p class="elsevierStylePara">Table 1. Equations for estimating GFR</p><p class="elsevierStylePara"><a href="grande/11101_16025_39619_en_t211101i2_copia.jpg" class="elsevierStyleCrossRefs"><img src="11101_16025_39619_en_t211101i2_copia.jpg" alt="Clinical and demographic characteristics"></img></a></p><p class="elsevierStylePara">Table 2. Clinical and demographic characteristics</p><p class="elsevierStylePara"><a href="grande/11101_16025_39621_en_t311101i2_copia.jpg" class="elsevierStyleCrossRefs"><img src="11101_16025_39621_en_t311101i2_copia.jpg" alt="Phase 1. Precision, bias, and accuracy"></img></a></p><p class="elsevierStylePara">Table 3. Phase 1. Precision, bias, and accuracy</p><p class="elsevierStylePara"><a href="grande/11101_16025_39623_en_t411101i2_copia.jpg" class="elsevierStyleCrossRefs"><img src="11101_16025_39623_en_t411101i2_copia.jpg" alt="Phase 2. Precision, bias, and accuracy"></img></a></p><p class="elsevierStylePara">Table 4. Phase 2. Precision, bias, and accuracy</p><p class="elsevierStylePara"><a href="grande/11101_16025_39624_en_t511101i2.jpg" class="elsevierStyleCrossRefs"><img src="11101_16025_39624_en_t511101i2.jpg" alt="Sensitivity and specificity of each equation to classify patients as having or not GFR less than 60ml/min/1.73"></img></a></p><p class="elsevierStylePara">Table 5. Sensitivity and specificity of each equation to classify patients as having or not GFR less than 60ml/min/1.73</p>" "pdfFichero" => "P1-E547-S3850-A11101-EN.pdf" "tienePdf" => true "PalabrasClave" => array:2 [ "es" => array:3 [ 0 => array:4 [ "clase" => "keyword" "titulo" => "Palabras clave" "identificador" => "xpalclavsec436871" "palabras" => array:1 [ 0 => "Ecuaciones de estimación del filtrado glomerular" ] ] 1 => array:4 [ "clase" => "keyword" "titulo" => "Palabras clave" "identificador" => "xpalclavsec436873" "palabras" => array:1 [ 0 => "Lupus eritematoso sistémico" ] ] 2 => array:4 [ "clase" => "keyword" "titulo" => "Palabras clave" "identificador" => "xpalclavsec436875" "palabras" => array:1 [ 0 => "Tasa de filtración glomerular" ] ] ] "en" => array:3 [ 0 => array:4 [ "clase" => "keyword" "titulo" => "Keywords" "identificador" => "xpalclavsec436872" "palabras" => array:1 [ 0 => "Equations" ] ] 1 => array:4 [ "clase" => "keyword" "titulo" => "Keywords" "identificador" => "xpalclavsec436874" "palabras" => array:1 [ 0 => "Systemic erythematosus lupus" ] ] 2 => array:4 [ "clase" => "keyword" "titulo" => "Keywords" "identificador" => "xpalclavsec436876" "palabras" => array:1 [ 0 => "Glomerular filtration rate" ] ] ] ] "tieneResumen" => true "resumen" => array:2 [ "es" => array:1 [ "resumen" => "<p class="elsevierStylePara"><span class="elsevierStyleBold">Antecedentes:</span> Ningún estudio ha podido determinar cuál es la mejor ecuación para calcular la función renal en pacientes con lupus eritematoso sistémico (LES) partiendo del análisis de una evaluación de referencia. <span class="elsevierStyleBold">Objetivo:</span> Evaluar el rendimiento de las ecuaciones basadas en cistatina/creatinina en la estimación de la función renal en pacientes con LES. <span class="elsevierStyleBold">Métodos:</span> Realizamos el estudio en dos fases: la primera incluía 14 pacientes en los que el aclaramiento de yotalamato se utilizó para determinar la tasa de filtración glomerular (FG) y se comparó con diferentes ecuaciones basadas en cistatina C y/o creatinina En la segunda fase, utilizamos la mejor ecuación (basada en cistatina y creatinina) como «estándar de referencia» para comparar 5 ecuaciones basadas en creatinina en 55 pacientes con LES. <span class="elsevierStyleBold">Resultados:</span> En la primera fase, la ecuación desarrollada por Stevens et al. (basada en creatinina y cistatina C) fue considerada la mejor. En la fase dos, la ecuación CKD-EPI (Chronic Kidney Disease Epidemiology Collaboration) fue considerada la mejor con una desviación de –2,1 ml/min/1,73, exactitud (P30 %) del 94,5 % y precisión (rango intercuartílico de las diferencias ) de –2,1 ml/min/1,73. <span class="elsevierStyleBold">Conclusiones:</span> Nuestros datos sugieren que la ecuación CKD-EPI es la mejor ecuación basada en creatinina para estimar la FG en pacientes con LES.</p>" ] "en" => array:1 [ "resumen" => "<p class="elsevierStylePara"><span class="elsevierStyleBold">Background:</span><span class="elsevierStyleBold"> </span>No study has determined the best equation for estimating renal function in patients with systemic lupus erythematosus (SLE), starting from a gold standard test. <span class="elsevierStyleBold">Objective:</span><span class="elsevierStyleBold"> </span>To evaluate the performance of cistatin/creatinine based equations for estimating renal function in patients with SLE. <span class="elsevierStyleBold">Methods:</span><span class="elsevierStyleBold"> </span>We conducted two phases: the first phase included 14 patients in which iothalamate clearance was used to determine the glomerular filtration rate (GFR) and compared with different equations based on cystatin C and/or creatinine. In the second phase, we used the best equation (a cystatin and creatinine-based equation) as “reference standard” to compare 5 creatinine-based equations in 55 patients with SLE. <span class="elsevierStyleBold">Results:</span><span class="elsevierStyleBold"> </span>In the first phase the equation developed by Stevens and colleagues (based on creatinine and cystatin C), was the best equation. In phase 2, the CKD-EPI (Chronic Kidney Disease Epidemiology Collaboration) equation was the best equation with bias of -2.1ml/min/1.73, accuracy (P30) of 94.5% and precision (interquartile range of differences) of -2.1 ml/min/1.73. <span class="elsevierStyleBold">Conclusions:</span><span class="elsevierStyleBold"> </span>Our data suggest that CKD-EPI is the best creatinine-based equation to estimate GFR in patients with SLE.</p>" ] ] "multimedia" => array:6 [ 0 => array:8 [ "identificador" => "fig1" "etiqueta" => "Fig. 1" "tipo" => "MULTIMEDIAFIGURA" "mostrarFloat" => true "mostrarDisplay" => false "copyright" => "Elsevier España" "figura" => array:1 [ 0 => array:4 [ "imagen" => "11101_16025_19898_en_f111101i6_copia.jpg" "Alto" => 2620 "Ancho" => 2106 "Tamanyo" => 952496 ] ] "descripcion" => array:1 [ "en" => "Difference between measured and estimated versus estimated GFR" ] ] 1 => array:8 [ "identificador" => "fig2" "etiqueta" => "Tab. 1" "tipo" => "MULTIMEDIAFIGURA" "mostrarFloat" => true "mostrarDisplay" => false "copyright" => "Elsevier España" "figura" => array:1 [ 0 => array:4 [ "imagen" => "11101_16025_39618_en_t111101i2_copia.jpg" "Alto" => 1339 "Ancho" => 2188 "Tamanyo" => 958037 ] ] "descripcion" => array:1 [ "en" => "Equations for estimating GFR" ] ] 2 => array:8 [ "identificador" => "fig3" "etiqueta" => "Tab. 2" "tipo" => "MULTIMEDIAFIGURA" "mostrarFloat" => true "mostrarDisplay" => false "copyright" => "Elsevier España" "figura" => array:1 [ 0 => array:4 [ "imagen" => "11101_16025_39619_en_t211101i2_copia.jpg" "Alto" => 522 "Ancho" => 2178 "Tamanyo" => 315586 ] ] "descripcion" => array:1 [ "en" => "Clinical and demographic characteristics" ] ] 3 => array:8 [ "identificador" => "fig4" "etiqueta" => "Tab. 3" "tipo" => "MULTIMEDIAFIGURA" "mostrarFloat" => true "mostrarDisplay" => false "copyright" => "Elsevier España" "figura" => array:1 [ 0 => array:4 [ "imagen" => "11101_16025_39621_en_t311101i2_copia.jpg" "Alto" => 763 "Ancho" => 2179 "Tamanyo" => 450238 ] ] "descripcion" => array:1 [ "en" => "Phase 1. Precision, bias, and accuracy" ] ] 4 => array:8 [ "identificador" => "fig5" "etiqueta" => "Tab. 4" "tipo" => "MULTIMEDIAFIGURA" "mostrarFloat" => true "mostrarDisplay" => false "copyright" => "Elsevier España" "figura" => array:1 [ 0 => array:4 [ "imagen" => "11101_16025_39623_en_t411101i2_copia.jpg" "Alto" => 461 "Ancho" => 2187 "Tamanyo" => 329307 ] ] "descripcion" => array:1 [ "en" => "Phase 2. Precision, bias, and accuracy" ] ] 5 => array:8 [ "identificador" => "fig6" "etiqueta" => "Tab. 5" "tipo" => "MULTIMEDIAFIGURA" "mostrarFloat" => true "mostrarDisplay" => false "copyright" => "Elsevier España" "figura" => array:1 [ 0 => array:4 [ "imagen" => "11101_16025_39624_en_t511101i2.jpg" "Alto" => 457 "Ancho" => 1077 "Tamanyo" => 162767 ] ] "descripcion" => array:1 [ "en" => "Sensitivity and specificity of each equation to classify patients as having or not GFR less than 60ml/min/1.73" ] ] ] "bibliografia" => array:2 [ "titulo" => "Bibliography" "seccion" => array:1 [ 0 => array:1 [ "bibliografiaReferencia" => array:35 [ 0 => array:3 [ "identificador" => "bib1" "etiqueta" => "1" "referencia" => array:1 [ 0 => array:3 [ "referenciaCompleta" => "Rahman A. Management of cardiovascular risk factors in patients with systemic lupus erythematosus. 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| Year/Month | Html | Total | |
|---|---|---|---|
| 2024 November | 14 | 14 | 28 |
| 2024 October | 81 | 43 | 124 |
| 2024 September | 77 | 46 | 123 |
| 2024 August | 90 | 61 | 151 |
| 2024 July | 66 | 49 | 115 |
| 2024 June | 85 | 43 | 128 |
| 2024 May | 88 | 42 | 130 |
| 2024 April | 80 | 29 | 109 |
| 2024 March | 66 | 28 | 94 |
| 2024 February | 59 | 36 | 95 |
| 2024 January | 42 | 36 | 78 |
| 2023 December | 58 | 29 | 87 |
| 2023 November | 61 | 31 | 92 |
| 2023 October | 64 | 31 | 95 |
| 2023 September | 60 | 40 | 100 |
| 2023 August | 57 | 31 | 88 |
| 2023 July | 87 | 25 | 112 |
| 2023 June | 78 | 30 | 108 |
| 2023 May | 63 | 32 | 95 |
| 2023 April | 57 | 20 | 77 |
| 2023 March | 62 | 20 | 82 |
| 2023 February | 53 | 22 | 75 |
| 2023 January | 47 | 19 | 66 |
| 2022 December | 58 | 19 | 77 |
| 2022 November | 63 | 46 | 109 |
| 2022 October | 58 | 39 | 97 |
| 2022 September | 62 | 22 | 84 |
| 2022 August | 70 | 49 | 119 |
| 2022 July | 50 | 42 | 92 |
| 2022 June | 66 | 46 | 112 |
| 2022 May | 100 | 49 | 149 |
| 2022 April | 117 | 41 | 158 |
| 2022 March | 152 | 76 | 228 |
| 2022 February | 171 | 64 | 235 |
| 2022 January | 126 | 63 | 189 |
| 2021 December | 85 | 51 | 136 |
| 2021 November | 78 | 45 | 123 |
| 2021 October | 63 | 48 | 111 |
| 2021 September | 40 | 45 | 85 |
| 2021 August | 42 | 57 | 99 |
| 2021 July | 60 | 67 | 127 |
| 2021 June | 69 | 50 | 119 |
| 2021 May | 75 | 46 | 121 |
| 2021 April | 153 | 46 | 199 |
| 2021 March | 60 | 43 | 103 |
| 2021 February | 61 | 33 | 94 |
| 2021 January | 59 | 24 | 83 |
| 2020 December | 45 | 20 | 65 |
| 2020 November | 54 | 14 | 68 |
| 2020 October | 34 | 14 | 48 |
| 2020 September | 60 | 22 | 82 |
| 2020 August | 58 | 21 | 79 |
| 2020 July | 62 | 18 | 80 |
| 2020 June | 52 | 16 | 68 |
| 2020 May | 51 | 13 | 64 |
| 2020 April | 49 | 20 | 69 |
| 2020 March | 49 | 16 | 65 |
| 2020 February | 43 | 16 | 59 |
| 2020 January | 63 | 22 | 85 |
| 2019 December | 73 | 15 | 88 |
| 2019 November | 44 | 20 | 64 |
| 2019 October | 33 | 6 | 39 |
| 2019 September | 40 | 19 | 59 |
| 2019 August | 30 | 8 | 38 |
| 2019 July | 56 | 16 | 72 |
| 2019 June | 61 | 25 | 86 |
| 2019 May | 78 | 27 | 105 |
| 2019 April | 85 | 36 | 121 |
| 2019 March | 60 | 18 | 78 |
| 2019 February | 55 | 24 | 79 |
| 2019 January | 32 | 28 | 60 |
| 2018 December | 94 | 36 | 130 |
| 2018 November | 167 | 25 | 192 |
| 2018 October | 170 | 15 | 185 |
| 2018 September | 205 | 14 | 219 |
| 2018 August | 110 | 25 | 135 |
| 2018 July | 110 | 17 | 127 |
| 2018 June | 101 | 21 | 122 |
| 2018 May | 93 | 19 | 112 |
| 2018 April | 100 | 4 | 104 |
| 2018 March | 87 | 11 | 98 |
| 2018 February | 82 | 7 | 89 |
| 2018 January | 84 | 6 | 90 |
| 2017 December | 84 | 11 | 95 |
| 2017 November | 117 | 18 | 135 |
| 2017 October | 66 | 9 | 75 |
| 2017 September | 85 | 11 | 96 |
| 2017 August | 91 | 13 | 104 |
| 2017 July | 75 | 15 | 90 |
| 2017 June | 84 | 8 | 92 |
| 2017 May | 99 | 18 | 117 |
| 2017 April | 77 | 15 | 92 |
| 2017 March | 83 | 7 | 90 |
| 2017 February | 130 | 29 | 159 |
| 2017 January | 74 | 17 | 91 |
| 2016 December | 127 | 3 | 130 |
| 2016 November | 173 | 18 | 191 |
| 2016 October | 210 | 12 | 222 |
| 2016 September | 399 | 6 | 405 |
| 2016 August | 348 | 8 | 356 |
| 2016 July | 282 | 13 | 295 |
| 2016 June | 178 | 0 | 178 |
| 2016 May | 202 | 0 | 202 |
| 2016 April | 133 | 0 | 133 |
| 2016 March | 112 | 0 | 112 |
| 2016 February | 133 | 0 | 133 |
| 2016 January | 144 | 0 | 144 |
| 2015 December | 161 | 0 | 161 |
| 2015 November | 136 | 0 | 136 |
| 2015 October | 116 | 0 | 116 |
| 2015 September | 96 | 0 | 96 |
| 2015 August | 87 | 0 | 87 |
| 2015 July | 95 | 0 | 95 |
| 2015 June | 55 | 0 | 55 |
| 2015 May | 63 | 0 | 63 |
| 2015 April | 14 | 0 | 14 |
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