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Medical Laboratory Technology Journal

Medical Laboratory Technology Journal

Available online at :

4 (2), 2018, 35-42

Evaluation of the i-STAT Blood Gas Analysis System in Cardiovascular Surgery

*Çiğdem Ünal Kantekin1,Müjgan Ercan2,Esra Fırat Oğuz3,Ertan Demirdaş4,Kıvanç Atılgan4, Mesut Sipahi5, Ferit Çiçekçioğlu4

1Department of Anesthesiology,Faculty of Medicine,University of Bozok,2Department of Bio- chemistry,Faculty of Medicine,University of Bozok, 3Biochemistry Laboratory,Ankara Numune

Training and Research Hospital,4Department of Cardiovascular Surgery Faculty of Medi- cine,University of Bozok,5Faculty of Medicine,University of Bozok Turkey

*Email: DOI: 10.31964/mltj.v%vi%i.190

Abstract: It is crucial to analyze the blood samples correctly and fast in open heart surgery.

Because of that, the reliability of point of care testing (POCT) analysis systems is an essential point for the clinician. This study aimed to investigate the compatibility of the parameters measured with the i-STAT blood gas analyzer and the conventional blood gas analyzer Rapid Point 500 (Siemens Healthcare Diagnostics, USA) in patients who underwent cardiovascular surgery. This clinical study included fifty patients undergoing coronary artery bypass surgery.

Fifty whole blood samples were portioned and measured on the i-STAT and RP500 laboratory analyzers — the compatibility between pH, pCO2, pO2, Hb, Na+, K+, iCa 2+ and glucose values investigated. There was a good correlation between the i-STAT analyzer with the RP500 analyzer, except Hb and Na+. Also, all parameters except for Hb and ionized calcium were found to be within acceptable range regarding clinical decision limits. It is essential that the point-of-care devices give accurate results as well as quick results. For this reason, we think that the point of care devices should be subject to external and internal quality control programs, users should be trained regularly, and feedback studies should be done.

Keywords: Blood gas analysis; Point of care testing; Hand-held portable analysis;Coronary artery bypass surgery; Operating room

Received 2018-11-17; Revised 2018-12-19; Accepted 2018-12-19


Blood gas analysis (BGA) is one of the most important tests used in emergency services, intensive and critical care units. Short turn around times is known to improve clinical outcomes by accelerating the decision-making process and patient care. The need for rapid laboratory test results leads to improve point of care testing (POCT) analysis systems as they are easy to use and cost effective. (Jatlow P.

2013, Parvin CA et al. 1996, Kilgore ML et al 1999, Price CP 2002)

Hand-held portable BGA systems are routinely used in some hospitals to provide more rapid, effective and also reliable results especially in critical care units (Nichols JH et al. 2000, Chin Pin Yeo et al. 2011, Dascombe

BJ et al. 2007). The i-STAT (Abbott Point of Care, East Windsor, NJ, USA) is one of the portable BGA analysis systems(Chin Pin Yeo et al. 2011). Sediame S et al. studied 92 routine blood gas samples of physiologically normal patients and found that results of i- STAT portable devices were reliable in comparison to conventional laboratory blood gas analyzer. Jacobs E et al. evaluated the performance of the i-STAT Portable Clinical Analyzer and found that the results of operator technique provided reliable results. (Sediame S et al. 1999, Jacobs E et al. 1993).


To our knowledge, there are only a few studies performed with critically ill patients.

Oyaert M et al. evaluated the analytical performance of a new cartridge type blood gas analyzer GEM Premier 5000 (Werfen) for pH, partial carbon dioxide pressure, and partial oxygen pressure determination. They emphasized that the evaluated device was suitable for both POCT and laboratory use (Oyaert M et al. 2018).

Lewis T C et al. studied 24 blood gas measurements including pH, partial pressure of oxygen and partial pressure of carbon dioxide to 5043 m. The samples were analyzed using an Abbott i-STAT blood gas analyzer and G3+ cartridges. They found that it is useful for both research and therapeutic measurements in remote, rural and wilderness medicine (Lewis T C et al. 2018).

In our study, we aimed to investigate the compatibility of the parameters measured with the i-STAT blood gas analyzer and the conventional blood gas analyzer Rapid Point 500 (Siemens Healthcare Diagnostics, USA) in patients who underwent cardiovascular surgery.


Whole blood from patients undergoing coronary artery bypass cases (n=50) collected and analyzed between January 2015 and January 2016. The patients were undergoing CABG with a beating heart or having a cardiac or non-cardiac simultaneous operation not included in this study. Fifty whole blood samples were portioned and measured on the i-STAT and RP500 laboratory analyzers. The evaluation of the i-STAT and Rapidpoint 500 laboratory analyzers were performed using 50 randomly collected samples with PICO50 lithium-balanced heparin whole blood syringe (Radiometer, Denmark) from CABG cases.

Two simultaneous blood gas samples were taken from each patient at any time. Thus, a total of 100 samples were collected from 50 patients.

Patients were informed about the study preoperatively, and their written consent obtained from the volunteer patients. One of the samples were first analyzed using the i- STAT in operation room, and the other sample was managed to reach laboratory by staff to perform the analyze with Rapidpoint 500 (Siemens Healthcare Diagnostics, USA).

This process took approximately 2–3 min for each sample. Results of Rapidpoint 500 determined via a calibration curve which is instrument-specifically generated by one-point (every 30 minutes) or 2-point calibration (every 2 hours). Results of i-STAT determined after calibration for each sample. Precision was determined using internal quality control samples, 20 runs performed in a day in duplicate for within-run precision and two runs per day in duplicate each for 20 days performed for between-day precision. Two levels of internal quality control materials are used for both devices every 8 hours a day.

The laboratory has an external quality control program attendance that the materials are studied once a month — the compatibility between pH, pCO2, pO2, Hb, Na+, K+, iCa2+

and glucose values investigated.

The i-STAT point-of-care laboratory system uses a single-use disposable cartridge containing chemically sensitive biosensors.

CG8+cartridge has a biosensor that consists of amperometric, potentiometric and conductometric circuits. The measurement of pH, pCO2, Na+, K+, ionized (iCa2+) performed with potentiometric ion-selective electrode (ISE) measurement; the amperometric electrodes used for the measurement of pO2 and glucose. The measurement of hematocrit (Hct) was performed with conductometric analysis.

Hemoglobin (Hb) is automatically calculated using the formula: Hb (g/dL) =Hct (% PCV) x 0.34.i-STAT Cartridges stored in the refrigerator at +4°C and before use, the sealed packaging was opened and left in the room for 5 minutes. The results are available in 2 minutes.

Laboratory testing performed on the Rapidpoint 500 (Siemens Healthcare Diagnostics, USA) for blood gas and electrolyte measurement. RP500 blood gas analyzer cartridges use the potentiometric measurement of pH, pCO2 Na+, K+, and iCa2+.

The amperometric electrodes used for measuring pO2 and glucose. Hemoglobin (Hb) measured by the co-oximetry method. Rapid Point 500 blood gas analyzer cartridges were kept in the room temperature (15-30 °C) until use. Every sample saved until the final output.

Both devices were kept side-by-side to preserve the equality of the environmental factors when the analysis performed.


Medical Laboratory Technology Journal For each sample, calibrations of devices,

automatic sample integrity, and quality controls performed before an operation.

Within-day and between-day precision studies were performed with the RP500 system. The i-STAT precision studies were calculated according to the data provided by the manufacturer.Duplicate measurements were done in method comparison studies. The study was approved by the Local Ethics Committee of Bozok University Faculty of Medicine and conducted according to the revised Declaration of Helsinki (1998).

The findings of this study were analyzed SPSS 18. The conformity of continuous variables to normal distribution was tested with the Kolmogorov–Smirnov test. The descriptive statistics of continuous variables were expressed as mean ± standard deviation for normal distributions. Linear regression analysis was performed for calculating bias (mean difference) and illustrated using Bland- Altman plots with the differences in parameter values between the methods plotted aganist their means. Total allowable error (TEA) and desirable bias based on within and between biological variations for each analytes were used (Ricos C et al 2014). Mean Bias was assessed using the formula: mean difference (%)=[(test tube mean-reference tube mean)/

reference tube mean x 100]( Ricos C, 2014).The statistical signifiance was calculated using pearson’s two-tailed t-test. P- value of <0.05 was considered statistically significant.Paired t-test and Wilcoxon test were used for parametric and nonparametric tests respectively:95% CI – confidence intervals of 95%.


The results of patient samples obtained from i-STAT and the reference device RP500 were shown in Table 1. The correlation cofficients (R) between the i-STAT and RP500 were >0.89 for each parameter,with the exception of Hb and Na+ (0.31, 0.57 respectively).The acquired resultsof parameters from Bland-Altman plots of the the i-STAT and RP500are shown inFigure 1.Statistically significant differences werefound for Hb(p=0.028), pH, pCO2, pO2, Na+, K+, Ca2+and glucose(all parameters p<0.001) between i-STAT and RP500.

The between-day and within-day precision of RP500 were shown in Table 2 and

Table 3. The CV % of all parameters of RP500 were < 2.42 .The precision values of i- STAT were shown in Table 4.

The blood gas parameters showed significant biases for pCO2, Hb,Na+, iCa2+andglucose parametres(mean bias-3.57%

desirable bias±1.8%,mean bias14.18%

desirable bias±1.84%, mean bias-1.73%

desirable bias±0.23%,mean bias-2.67%

desirable bias±0.6%, mean bias-2.17%

desirable bias±1.8%) respectively (Table 1).

Lower and upper limits of clinical insignificant difference calculated based on total allowable error (TEa) in RP500 parameters were shown in Table 5, i-STAT parameters were within the indicated limits, with the exception of Hb and iCa2+ (Table 5).

In the present study, we compared the results of electrolytes, pH, blood gases, Hb and metabolites in whole blood measured bythe i- STAT analyzer (Abbott Point of Care, East Windsor, NJ, USA) andconventional laboratoryblood gas analyzer (Rapid Point 500, Siemens Healthcare Diagnostics, USA).

Also our study compared the correlation between the i-STAT and RP500. These two analyzersshowedhigh correlation (R>0.89) except Na+ (R=0.57) and Hb (R=0.31).

In a study the analysers of i-STAT and Central Laboratory were compared and there were similar correlation coefficients with the results ofour study(Na+R=0.56)( Chin Pin Yeo et al 2011).These findings were in contrast to the previously reported excellent results (R=0.84–0.99) between the epocdevice and the i-STAT(Stotler BA,Kratz A 2013,Steinfelder -Vischer J et al 2008,Papadea C et al 2002). In terms of Hb results, we found bad correlation (R=0.31) however other studies showed better correlation results (Luukkonen AA et al 2015,Leino A, Kurvinen K 2011).

Although there were low data between two analyzers in terms of the significance of the mean bias, some studies detected the difference of bias based on the biological variaton database and the external quality control data (Luukkonen AA et al 2015).

Significant differences in Hb, pCO2, glucose values were determined according to the desirable biological variaton database. Despite the absence of desirable bias , the data was evaluated based on the bias value from external quality results and a significant difference was detected.


pH p02 pC02 Hb Na+ K+ Ca+2 Glucose

Unit mmHg mmHg g/dL mmol/L mmol/L mmol/L mg/dL

Slope# 1,02x (-0,94~


1,046x (0,97~


1,003x (0,90~


0,13x (0,015~


0,969x (-48,91~


0,992x (0,93~


1,080x (0,92~


1,020x (0,98~

1,05) y-intercept#

-0,202 (0,93~


-13,19 (-21,93~


1,234 (-2,35~




6,727 (0,56~


0,014 (-0,23~


-0,052 (0,92~


0,340 (-6,72~

7,40) RP500 7,45**

(7.22~7.60 )




(27.1~60.8 )


(5.4~10.3) 139,54±6.89

* 4,02±0.64

* 1,12±0.10

* 176,98**

(101~359) i-STAT 7,44**

(7.20~7.54 )


(27.5~358.4 )


(23.9~61.1 )


(7.8~16.03 )


* 4,05±0.63

* 1,09±0.08

* 173,22**

(95~348) Agreemen

t mean ¥

0,011 (0,003~


-8,50 (-13,9~3,10)

1,32 (0,64~2)

-1,55 (-2,98~


2,42 (0,81~4,03)

-0,02 (-0,06~


0,04(0,02~0,05 )

3,76 (1,25~6,26 )

Mean Bias

(%) -0,013 9,50 -3,57 14,18 -1,73 0,74 -2,67 -2,17


bias (%)** - - ±1,8 ±1,84 ±0,23 ±1,81 ±0,6 ±1,8


e limit ±0,11 ±12,91 ±1,79 - ±0,54 ±0,66 ±2,58 ±0,07

R 0,95 0,97 0,94 0,31 0,57 0,97 0,89 0,99

p-value* <0,001 <0,001 <0,001 0,028 <0,001 <0,001 <0,001 <0,001 Range of

results 7,20~7,60 27,5~425 23,9~61,1 5,4~16,03 128~178 2,8~5,8 0,85~1,42 95~359

N 50 50 50 50 50 50 50 50

*p-value was calculated by pearson’s two tailed correlation test.**Mean Bias was assessed using the formula: mean difference (%) = [ (test tube mean - reference tube mean) / reference tube mean x 100 ]. Desirable bias based on within and between biological variations (11). .

*Mean±SD; **Median (min-max):Paired t-test and Wilcoxon test were used for parametric and nonparametric tests respectively:95% CI – confidence intervals of 95%.

Table 1. Correlation statisticis between RP500 and i-STAT

Parameter Level 1

Mean SD CV% Level 2

Mean SD CV%

pH 7,12 - 7,31 -

p02 (mmHg) 148,41 1,48 0,99 102,57 1,52 1,48

pC02(mmHg) 69,99 1,69 2,42 42,62 0,73 1,72

Hb(g/dL) 18,09 0,07 0,40 13,9 0,047 0,33

Na+ (mmol/L) 118,65 0,37 0,31 142,86 0,29 0,20

K+(mmol/L) 3,26 0,006 0,21 5,30 0,02 0,38


L) 1,65 0,012 0,75 1,29 0,006 0,53


dL) 189,6 1,26 0,66 94,4 0,84 0,89

Table 2. Precision for between-day of RP500 system


Medical Laboratory Technology Journal


Level 1

Mean SD CV% Level 2

Mean SD CV%

pH 7,11 0,003 0,04 7,30 0,002 0,02

pO2 (mmHg) 151,9 0,48 0,31 103,78 0,93 0,90

pCO2(mmHg) 71,52 0,31 0,43 43,26 0,30 0,71

Hb(g/dL) 18 - - 13,85 0,05 0,38

Na+ (mmol/L) 118,5 0,2 0,16 142,09 0,15 0,17

K+(mmol/L) 3,23 0,013 0,40 5,28 0,04 0,78


L) 1,66 0,004 0,26 1,31 0,004 0,78


dL) 191,2 1,78 0,93 94,7 0,82 0,86

Level 1 Level 2

Parameter Mean SD CV% Mean SD CV%

pH 7.165 0.005 0.08 7.656 0.003 0.04

pO2 (mmHg) 65.1 3.12 4.79 146.5 6.00 4.10

pCO2(mmHg) 63.8 1.57 2.5 19.6 0.40 2

Hb (g/dL) 10.2 0.44 1.5 16.66 0.50 1.0

Na+ (mmol/L) 120.0 0.46 0.4 160.0 0.53 0.3

K+(mmol/L) 2.85 0.038 1.3 6.30 0.039 0.6

iCa+2(mmol/L) 1.60 0.017 1.1 0.84 0.012 1.4

Glucose (mg/dL) 41.8 0.68 1.6 289 2.4 0.8

Table 3. Precision for within-day of RP500 system

Table 4. The precision values of i-STAT

In a study comparing the results of the reference method with i-STAT from patients undergoing cardiopulmonary bypass and patients in intensive care units, they reported a significant difference for pO2 values (Stotler BA, Kratz A. 2013). In another study researchers found that the mean biases of pO2 were statistically significant (Steinfelder-Visscher J et

al 2008). In our study we did not find any difference in terms of pO2 values but significant difference was found for pCO2, Hb, Na+, iCa2+

and glucose parameters according to desirable mean, based on the acceptable bias data from OneWorld Accuracy External Quality program (Table 1).


Table 5. Comparison of results obtained from i-STAT device within the Clin.Low and Clin.Up calculated based on TEA% of reference RP500

TEA: Total allowable error.(11)Based on theseTEAClin.Low: lower limit of clinically insignificant difference and Clin.Up: upper limit of clinically insignificant difference were calculated.

Hb levels in i-STAT calculated via Hct measured by the conductometry system. Hct analysis has been shown in many studies that it methodically led to interference (Stott RA et al. 1995). The low protein concentration leads to low Hb values due to the significant negative bias in Hct measurement. Also, the reduction of the total conductivity in the electrolytes and colloid-containing infusions affects the result (Stott RA et al. 1995).

We observed a quite high mean difference in calculated Hb(%14)between the i -STAT and the Rapid Point 500 analyzer (Table 1).In case of measuring Hb levels lower than real levels may cause unnecessary intraoperative blood transfusion and a volume overload which can result with serious complications such as intraoperative cardiac insufficiency, hemodynamic instability. And in case of measuring it higher than normal levels and deficient blood transfusion would result with inadequate tissue oxygenation.

In the study comparing three different blood gas analyzers (EPoC, RL1265, and RP500) they found the significant mean difference in Hb values measured with the three analyzers, similar to the results of our

study (Luukkonen AA et al. 2015). Also, abnormal electrolyte levels may cause incorrect Hb results as shown in the study used samples from patients undergoing CABG. They found a decrease in the conductivity of samples of these patients and they suggest that the decrease could affect the conductometric measurement of Hb (Steinfelder-Vischer J, 2008). The variation of Hb results of our study could be derived from the altered conductivity of the samples of patients undergoing CABG.

One of the most affected parameters of preanalytical factors (such as air contamination, low volume or drug use (propofol, thiopental sodium) is pCO2. The significant difference of pCO2 values could be attributed to the fact that it is easily affected by preanalytical errors.

In a study, they found that all parameters except lactate, Hb, Na+, and pCO2

were within acceptable limits according to TEa (Luukkonen AA et al. 2015). Similarly, in our study, all parameters, except Hb and iCa2+

were found to be within the indicated limits according to TEa.

One of the limitations of our study was our results can be only limited to a particular patient group. Also, studies with larger sample groups could provide more information as we had a smaller sample group size.


In conclusion, according to our results, there was a good correlation between the i- STAT analyzer with the RP500 analyzer, except Hb and Na+. Also, all parameters except for Hb and ionized calcium were found to be within acceptable range regarding clinical decision limits. It is essential that the point-of- care devices give accurate results as well as quick results. For this reason, we think that the point of care devices should be subject to external and internal quality control programs, users should train regularly, and feedback studies should be doing.

TEA, % RP500


Clin.Up i-STAT

pH - 7,45

- 7,44

p02 (mmHg) - 93,95

- 102,46

pC02 (mmHg) ±5,7 37,25

35,13-39,37 35,92 Hb (g/dL) ±4,19 10,93

10,48-11,38 12,48 Na+ (mmol/L) ±0,73 139,54

138,53-140,55 137,12 K+ (mmol/L) ±5,61 4,02

3,8-4,24 4,05

iCa+2 (mmol/L) ±2 1,12

1,10-1,14 1,09

Glucose (mg/

dL) ±5,5 176,98

167,25-186,71 173,22


Medical Laboratory Technology Journal

Figure1 :Bland-Altman plots for the comparison of RP500 and i-STATresults. The y-axis represents the difference between RP500 and the comparison method i-STAT (RP500 – i- STAT), and the x-axis represents the average of RP500 and i-STAT values. Horizontal line sare drawn at themean difference (blue), at the mean difference ±1.96 SD (95% confidence intervals) of the differences (green dotted line).



Chin Pin Yeo , Adeline Ngo , Wai Yoong Ng , Swee Han Lim, Edward Jacob (2011).

Assessing Performance of i-STAT at the Point of Care in the Emergency Room Proceedings of Singapore. Healthcare, 20, 157–161.

Dascombe BJ, Reaburn PR, Sirotic AC, et al.

(2007). The reliability of the i-STAT clinical portable analyser. J Sci Med Sport, 10, 135–140.

Jacobs E, Vadasdi E, Sarkozi L, Colman. N.

(1993). Analytical evaluation of i-STAT portable clinical analyzer and use by non laboratory health care professionals. Clin Chem, 39, 1069–74.

Jatlow P. (1993). Point of care laboratory test- ing in the emergency department. Am J Clin Pathol, 100, 591.

Kilgore ML, Steindel SJ, Smith. JA. (1999).

Cost analysis for decision support: the case of comparing centralized versus dis- tributed methods for blood gas testing. J Healthc Manag, 44, 207–215.

Leino A, Kurvinen. K. (2011). Inter change ability of blood gas, electrolyte and metabolite results measured with point-of- care, blood gas and core laboratory analyzers. Clin Chem Lab Med, 49, 1187–


Lewis TC, Malein WL, Chesner I, Clarke. S.

(2018). Birmingham Medical Research Expeditionary Society (BMRES). High alti- tude arterialised capillary earlobe blood gas measurement using the Abbott i- STAT. JR Army Med Corps, 164, 335–37.

Luukkonen AA , Lehto TM , Hedberg PS, Vaskivuo. TE. (2016). Evaluation of a hand-held blood gas analyzer for rapid determination of blood gases, electrolytes, and metabolites in intensive care setting.

Clin Chem Lab Med, 4, 585–594.

Nichols JH, Kickler TS, Dyer KL, Humbertson SK, Cooper PC, Maughan WL, et al.

(2000). Clinical outcomes of point-of-care testing in the interventional radiology and invasive cardiology setting. Clin Chem, 46, 543–50.

Oyaert M, Maerken TV, Bridts S,Loon SV,Laverge H, S. V. (2018). Analytical and pre-analytical performance character- istics of a novel cartridge-type blood gas analyzer for point-of-care and laboratory testing. Clinical Biochemistry, 53, 116–26.

Parvin CA, Lo SF, Deuser SM, Weaver LG, Lewis LM, Scott. MG. (1996). Impact of point-of-care testing on patients’ length of stay in a large emergency department.

Clin Chem, 42, 711–7.

Price CP. (2002). Medical and economic out- comes of point-of-care testing. Clin Chem Lab Med, 40, 246-51.

Ricos C, Alvareq V, Cava F, Garcia-Lario JV, Hernandez A, Jimenez CV, et al. (2014).

Desirable specifications for total error, im- precision, and bias, derived from intra- and inter-individual biological variation.

Retrieved from

biodatabase 2014-update.htm.

Sediame S, Zerah-Lancner F, d’Ortho MP, Ad- not S, Harf. A. (1999). Accuracy of the i- STAT bedside blood gas analyser. EurRe- spir J, 14, 214–7.

Steinfelder-Vischer J, Teerenstra S, Gunnewiek JM, Weerwind PW.,(2008).

Evaluation of the i-STAT point-of-care- analyzer in critically ill adult patients. J Extra Corpor Technol. 40:57–60.

Steinfelder-Vischer J, Teerenstra S, Gunnew- iek JM, Weerwind. PW. (2008). Evaluation of the i-STAT point-of-care-analyzer in critically ill adult patients. J Extra Corpor Technol, 40, 57-60.

Stotler BA, Kratz A.(2013). Analytical and clinical performance of the epoc blood analysis. Am J Clin Pathol,140, 715–20.

Stott RA, Hortin GL, Wilhite TR, Miller SB, Smith CH, Landt. M. (1995). Analytical ar- tifacts in hematocrit measurements by whole-bloodchemistry analyzers. Clin Chem, 41, 306–11.


Medical Laboratory Technology Journal

Medical Laboratory Technology Journal

Available online at :

4 (2), 2018, 43-48

Colposcopy results in Smear negative, High-risk HPV positive patients

Deha Denizhan Keskin

Department of Obstetrics and Gynecology, Ordu University of Medical Faculty, Education and Research Hospital, Ordu, Turkey.

Email: DOI: 10.31964/mltj.v4i2.189

Abstract: Cervix cancer is an HPV (Human papillomavirus) related cancer, and HPV positivity is necessary even if there is no cytology abnormality. We aimed to determine the ratios of 13 high-risk HPV types in cases with high-risk HPV positivity without cervical smear pathology re- ferred to our clinic and to determine the relation of HPV types with age, parity, menopausal sta- tus, and abnormal histopathological results. Two hundred forty-one cases included in the study, which referred to us because of HPV positivity and colposcopically biopsied between January 2014 to January 2018. HPV prevalences were investigated. The relationship between HPV types and variables such as age, parity, menopausal status examined. The mean age of 241 patients included in the study was 46,1+8,8. The parity average was 2,4+1,1. Sixty-five of the patients (27%) were postmenopausal. Of the 241 HPV-positive patients, 172 (71,4%) had only high-risk HPV viruses. The frequency ranking of HPV types was as follow; 16, 31, 51, 56, 18, 52, 35, 58, 39, 68, 45, 33 and 59. According to the HPV types, the average ages were as fol- low; 18 (43,6 years), 33 (40,1 years) and 51 (41,9 years) were younger than the average age.

35 (48,7 years), 39 (48,5 years), 52 (49,1 years) and 68 (51,3 years) were older than the aver- age age. 16 (44,9 years), 31 (47,9 years), 45 (44,3 years), 56 (47,3 years), 58 (46,9 years) and 59 (46,7 years) was similar the average age. There was no significant difference between the parities according to HPV types (2 to 2,7). According to the HPV types, the menopausal state was as follows; 39 (50%), 56 (50%) and 68 (53,8%) mostly observed in the postmenopausal period; A small proportion of 33 cases (12,5%) was postmenopausal. The rate of severe dys- plasia according to colposcopic biopsy related with HPV types was; 58 (40%), 56 (30,8%), 18 (28%), 45 (27,3%), 31 (26,1%), 39 (25%), 59 (16,7%), 35 (14,3%), 51 (13,8%), 33 (12,5%), 16 (11,8%), 52 (8,3%). The prevalence of HPV types, the age at which they saw, the menopausal status and the potential for the formation of severe dysplasia are highly variable. We think that routine screening programme, colposcopy indications and vaccination program should cover all HPV types according to data.

Keywords: Cervical cancer; HPV positivity; Smear negativity

Received 2018-11-26; Revised 2018-12-17; Accepted 2018-12-19


Various infectious agents, especially Hepatitis B (HBV), Hepatitis C (HCV), Human papillomavirus (HPV) and Helicobacter pylori, account for 23% of the causes of human can- cer (WHO Global Cancer Report, 2003).

Among HPV infectious agents, it is essential because of the most frequent association with cancer and the most common sexually trans- mitted disease (Depuydt et al., 2016; Chattzis- tamatiou et al., 2016; Kim, 2017). HPV is a small double-stranded DNA virus that has been described more than 200 types daily.

This difference provides the genetic sequence of the outer capsid protein L1. It has been

found that about 40 of this family of viruses go through sexual contact and cause infection in the basal epithelium layer of the genital muco- sa in both men and women (CDC HPV report, 2015; Sah et al., 2018). HPV infections heal spontaneously within 1 to 2 years in 70-90%

(Cubie, 2013). Persists are known to cause vulvovaginal, penile, anal, head and neck can- cers, especially cervical cancer with oncogenic effect (Depuydt et al., 2016; ICO/IARC HPV and Related Diseases Report, 2017).

HPV viruses are divided into two groups as low risk (LR HPV) and high (HR HPV) risk compared to cancer development potentials.


International cancer research agency (IARC) recently identified 25 HR HPV in 2012. These are in turn; 16, 18, 26, 30, 31, 33, 34, 35, 39, 45, 51, 52, 53, 56, 58, 59, 66, 67, 68, 69, 70, 73, dir (Humans Biological agents, 2012).

Cervical cancer among HPV-related dis- eases is the fourth most common cancer seen in women worldwide — the second most com- mon cancer in women aged 15 to 44 years.

Every year more than 500,000 women are di- agnosed, and approximately 265,000 women die from this cancer (ICO/IARC HPV and Re- lated Diseases Report, 2017).

When we work on this information, we try to show the frequency of HPV types in our region. We also investigated the relationship between HPV types and age, menopausal sta- tus, and abnormal histopathology results.


Ordu Provincial Health Directorate and Ordu University Medical Faculty Training and Research Hospital Clinical Practice Ethics Committee approvals obtained (Date:

26/04/2018, Number: 2018-73). My study in- cluded 241 patients between January 2014 and January 2018 who underwent colposcopic biopsy with a normal cytology referred to ours due to HPV positivity. Patients with HPV and cytologic examinations made by the Center for Cancer Early Diagnosis Screening and Train- ing (KETEM), a national screening organiza- tion. In this organization women aged between 30 and 65 years are invited for HPV based screening by family physicians every five years. Two samples are taken from each wom- an to enable cytology testing in those found to be HPV positive without the need for a sepa- rate visit. The first sample is collected with a brush and transferred to a glass slide for con- ventional cytology. The second is taken with a different brush and put into 5 ml of Standard Transport Medium for HPV DNA analysis. And the result report is sent to the medical profes- sional to be shared with the patient. The col- poscopy examination and biopsy results of the

patients were retrospectively scanned and re- trieved from the hospital registry system

The 13 high oncogenic HPV types ex- amined by KETEM were as follows; 16, 18, 31, 33, 35, 39, 45, 51, 52, 56, 58, 59, 68. For women who are HPV positive by Hybrid Cap- ture2 (Qiagen), genotyping performed with the CLART kit (Genomics). HPV results were ana- lyzed, and the prevalences of HPV types sub- tracted. The relationship between variables such as age, parity, menopausal status and HPV types was examined.

In progressing cervical cancer, cervical preinvasive lesions are significant. According to the classic classification of cervical preinva- sive lesions; colposcopic biopsy results classi- fied as Normal, CIN I, CIN II, CIN III, and CIS (cervical carcinoma in situ). CIN II and ad- vanced cases (which have high risk potential for cervical cancer) were evaluated as severe dysplasia. The rates of severe dysplasia of HPV types investigated.

All data analyses were done by SPSS 20.0. One Way ANOVA and T-Test were used due to group number to analyze data consist with independent measurements showing nor- mal distribution. Pearson Correlation Test was used to determine the relation¬ship between the groups. To analyze the variations that do not distribute normally, Kruskal Wallis and Mann-Whitney U tests used. Spearman Corre- lation Test was used to determine relationship between the groups. Data in a categorical structure analyzed by Chi square test. A p- value <0.05 was accepted as significant.


The mean age of 241 patients included in the study was 46.1 + 8.8. The parity average was 2.4 + 1.1. Sixty-five of the patients (27%) were postmenopausal. Of the 241 HPV- positive patients, 172 (71.4%) had single, 48 (19.9%) had two, 15 (6.2%) had three, 5 (2.1%) had four, 1 (0.4%) had five high-risk HPV viruses were detected. (Table 1 and 2).

The mean age 46.1 + 8.8

The parity average 2.4 + 1.1

The postmenopausal ratio 27% (65/241)

Table 1. The General Information


Medical Laboratory Technology Journal Table 2. High-risk HPV Distribution

One high-risk HPV 172 patients 71.4%

Two high-risk HPV 48 patients 19.9%

Three high-risk HPV 15 patients 6.2%

Four high-risk HPV 5 patients 2.1%

Five high-risk HPV 1 patient 0.4%

Table 3. The frequency ranking and the mean ages of HPV types (According to the incidence)

16 93 44.9

31 46 47.9

51 29 41.9 (younger)

56 26 47.3

18 25 43.6

52 24 49.1 (older)

35 21 48.7

58 20 46.9

39 16 48.5

68 13 51.3 (older)

45 11 44.3

33 8 40.1 (younger)

59 6 46.7

Table 4. The rate of detection of severe dysplasia after colposcopic biopsy (According to the severity)

58 40%

56 30.8%

18 28%

45 27.3%

31 26.1%

39 25%

68 23.1%

59 16.7%

35 14.3%

51 13.8%

33 12.5%

16 11.8%

52 8.3%

total 27.8%


The frequency ranking of HPV types was as follows; 16 (93 patients), 31 (46 pa- tients), 51 (29 patients), 56 (26 patients), 18 (25 patients), 52 (24 patients), 35 (21 pa- tients), 58 (20 patients), 39 (16 patients), 68 (13 patients), 45 (11 patients), 33 (8 patients), and 59 patients (6 patients).

According to the HPV types, the mean age was as follows; 33 (40.1) and 51 (41.9) were younger than the average age. 52 (49,1) and 68 (51,3) were older than the average age. 16 (44.9), 18 (43.6), 31 (47.9), 35 (48,7), 39 (48,5), 45 (44.3), 56 (47.3), 58 (46.9) and 59 (46.7) was observed like the average age.

(Table 3). There was no significant difference between the parties according to HPV types (2 to 2.7). (p>0.05).

According to the HPV types, the meno- pausal state was as follows. 39 (50%), 56 (50%) and 68 (53.8%) were observed in the postmenopausal period; A small proportion of 33 cases (12.5%) were postmenopausal.

The rate of detection of severe dyspla- sia after colposcopic biopsy was 67/241 (27,8%). The rates of severe dysplasia accord- ing to HPV types were as follows; 58 (40%), 56 (30.8%), 18 (28%), 45 (27.3%), 31 (26.1%), 39 (25%), 68 (23.1), 59 (16,7%), 35 (14,3%), 51 (13,8%), 33 (12,5%), 16 (11,8%), 52 (8,3%). (Table 4).

The HPV DNA testing now has shown as the primary screening program by many or- ganizations, notably the World Health Organi- zation (WHO) and the International Agency for Research on Cancer (IARC). In cervical can- cer screening, many high-income countries such as Norway, the Netherlands and Austral- ia now use HPV DNA testing in cancer screen- ing programs instead of conventional cervical smear screening (Anttila et al.,2015; Huh et al., 2015). Turkey cervical cancer screening program based on data from the population screening HPV DNA test screening 5 - 6 points increase reported to provided. It has also been shown that this program has additional ad- vantages, such as less human workload, faster results, less need for sampling, and fewer hos- pital visits (Gultekin et al., 2018).

The prevalence of HPV in the world is highly variable compared to geographical re- gions. The HPV frequency in the community is around 10% (1.4% - 25.6%). Also, 95% to 100% of patients with cervical cancer have a close relationship with the virus and cancer

(Walboomers et al., 1999; Basu et al., 2011;

Kirschner et al., 2013).

In Turkey, there are many studies demonstrating the HPV prevalence (2% - 25%). In a retrospective analysis of 6388 pa- tients who referred to member centers for the Turkish gynecologic oncology group, the HPV positivity rate was 25%. The high prevalence in this study can be attributed to the fact that the centers participating in the study are refer- ee oncologic centers (Dursun et al., 2013). 30 - a million women are screened from age 65 for Turkey, according to the results of cervical cancer screening and HPV positivity in our country was 3.5% (Walboomers et al., 1999).

On the other hand, common HPV types also vary from region to region. The 5 most common types of HPV were 16, 53, 52, 18, 39;

In Europe 16, 18, 31, 33, 58; In Asia there are 16, 52, 58, 18, 56 (Barut et. al., 2018). Turkey study also showed that Turkey's peculiar distri- bution; 16 (20,7%), 51 (10,8%), 31 (8,7%), 52 (7,1%), 56 (5,7%). For example, the uncom- mon type observed in other regions 51 to the second common in Turkey. Such as having a high oncogenic Type 16 18 Turkey ranks sev- enth in the study. Also common in North Amer- ica, Asia and type 52 and type 31, which is still in the top five in Europe is often seen in Tur- key shows that we are a mosaic of countries regarding HPV (Walboomers et al., 1999).

In our study, the first five HPV types were as follows; 16 (38,5%), 31 (19,1%), 51 (12%), 56 (10,8%) and 18 (10,4%). We found some differences sailed close to Turkey as well as the general data of our study data.

Prevalence of Turkey was 12.6% higher than other studies because the data in Turkey (13 HR HPV types external olds) HPVs not di- rected by us. In addition, depending on the type of high referral rates by 16 family physi- cians and 18 types, we found extremely high data rates compared to Turkey. We also had our differences in the method of operation with Turkey. Turkey abnormal cytology 19.1% of HPV-positive patients in the studies of HPV positivity we left off work to show the inde- pendent effects of abnormal cytology. Finally, we found that type 31 was higher than type 51 when we were working on a possible regional difference.

In our study, we found that HPV 16 and 18 positivity observed in 46.9% of patients.


Medical Laboratory Technology Journal However, we believe that 16 and 18 non-HR

HPV ratios are quite high, and 16 and 18 of the potential for severe dysplasia considered.

The incidence of single / multiple HPV preva- lence, such as HPV prevalence, was also found at quite different rates in studies. In a large-scale Chinese study, 79% of the cases had a uniform infection rate, while a recent study from our country yielded multiple infec- tion rates of 59% (Barut et al., 2018; Tao et al., 2018). In our study, the odd infection rate was more than 71.4%.

According to HPV types, 18, 33, 51 ob- served at younger ages than mean age; 35, 39, 52, 68 were older than the average age.

On the other hand, there was no significant difference between the parities according to HPV types (2 to 2.7). Also, more than half of 39, 56 and 68 cases observed in the postmen- opausal period; A relatively small proportion of 33 cases (12.5%) were postmenopausal.

The HPV test scans the conventional smear. One million women screened by Tur- key in 3499 women working colposcopy di- rected in 1869 (53.4%) observed in any cervi- cal smear abnormalities. However, colposcop- ic biopsy results showed 708 CIN I (20,2%), 285 CIN II (8,1%), 436 CIN III (12,4%) and 85 cancer (2,4%). The most important outcome of the study was the ability to skip 45.9% of CIN III and advanced cases with conventional smear scanning (Walboomers et al., 1999).

The incidence of severe dysplasia in Turkey operating in the HPV positivity in our study, while 22.9% of patients with pathology cervical smear to exclude Although we rate rose to 27.8%.

In our study, we also performed a re- view of the species. The rate of severe dyspla- sia after colposcopic biopsy was according to HPV types. 58 (40%), 56 (30.8%), 18 (28%), 45 (27.3%), 31 (26.1%), 39 (25% 59 (16,7%), 35 (14,3%), 51 (13,8%), 33 (12,5%), 16 (11,8%), 52 (8,3%). Although we observed very little severe dysplasia in Type 16 positivi- ty, we think it is very important because of the most common type of HPV.

It is known that HPV screening, as well as studies on HPV vaccines, are continuing rapidly. HPV 16 and 18 were shown to be re- sponsible for 70% of cervical cancer. And it is said that HPV 31, 33, 45, 52 and 58 are an ad- ditional 22% of cancer cases. And in our study

we showed that HPV 58, 45 (due to the high tendency to dysplasia) and 31, 51, 56 (due to frequent occurrence) deserves more attention;

like HPV 16 and 18. Vaccine studies are car- ried out in this framework. Cervarix® (16,18) and Gardasil® (6,11,16,18) are the first vac- cines and protect against only 46.9% of HPV types in our cases. Finally, Gardasil 9®

(6,11,16,18,31,33,45,52,58) has been applied to the market and is protective against 84.6%

of the HPV types in our study.

We think that vaccine programs should develop rapidly and produce new generations of vaccines containing only the most common types in European countries - inclusive low and middle-income countries where deaths from the entire world - more often cervical can- cer deaths - are more common.


Although reflex cytology suggested in the literature with HPV 16-18 non-HR HPV positivity, we advocate the necessity of direct colposcopy from these cytologic follow-ups.


Anttila A, Arbyn M, Vuyst de H, et al., eds.

(2015). European Guidelines for Quality Assurance in Cervical Cancer Screening, 2nd edn., Supplements. . Luxembourg: Eu- ropean Union Publications.

Barut MU, Yildirim E, Kahraman M, Bozkurt M, Imirzalioğlu N, Kubar A, Çalişkan E, Sak S, Aksu. T. (2018). Human Papilloma Viruses and Their Genotype Distribution in Women with High Socioeconomic Status in Central Anatolia, Turkey: A Pilot Study. Medical Science Monitor, 24, 58–66.

Basu P, Chandna P, Bamezai RN, Siddiqi M, Saranath D, Lear A, Ratnam. S. (2011).

MassARRAY spectrometry is more sensi- tive than PreTect HPV-Proofer and consen- sus PCR for type-specific detection of high- risk oncogenic human papillomavirus geno- types in cervical cancer. Journal of Clinical Microbiology, 49, 3537–3544.

Centers For Disease Control And Prevention.

CDC. (n.d.). The Pink Book Home. In Hu- man Papillomavirus. Retrieved from https://



Chatzistamatiou K, Moysiadis T, Moschaki V, Panteleris N, Agorastos. T. (2016). Com- parison of cytology, HPV DNA testing and HPV 16/18 genotyping alone or combined targeting to the more balanced methodolo- gy for cervical cancer screening. Gyneco- logic Oncology, 142, 120–7.

Cubie HA. (2013). Diseases associated with human papillomavirus infection. Virology, 445, 21–34.

Depuydt T, Beert J, Bosmans E, Salembier. G.

(2016). Human papillomavirus (HPV) virion induced cancer and subfertility, two sides of the same coin. Facts Views & Visions in Obgyn, 8, 211–222.

Dursun P, Ayhan A, Mutlu . L. et al. (2013).

HPV Types in Turkey: Multicenter Hospital Based Evaluation of 6388 Patients in Turk- ish Gynecologic Oncology Group Centers.

Turkish Journal of Pathology, 29, 210–6.

Gultekin M, Karaca MZ, Kucukyildiz I, Dundar S, Boztas G, Turan HS, Hacikamiloglu E, Murtuza K, Keskinkilic B, Sencan I. (2018).

Initial results of population based cervical cancer screening program using HPV test- ing in one million Turkish women. Interna- tional Journal of Cancer, 142, 1952–1958.

Huh WK, Ault KA, Chelmow D, et al. (2015).

Use of primary high-risk human papilloma- virus testing for cervical cancer screening:

interim clinical guidance. Gynecol Oncol, 136, 178–182.

Human Papillomavirus and Related Diseases Report. (2017). ICO/IARC Information Cen- tre on HPV and Cancer. Retrieved from


Humans IWGotEoCRt. (2012). Biological agents. Volume 100 B. A review of human carcinogens. IARC Monogr Eval Carcinog Risks Hum, 100, 1–441.

Kim HJ. (2017). Current status and future pro- spects for human papillomavirus vaccines.

Archives of Pharmacal Research, 40, 1050 -63

Kirschner B, Junge J, Holl K, Rosenlund M, Collas de Souza S, Quint W, Molijn A, Jen- kins D, Schledermann. D. (2013). HPV genotypes in invasive cervical cancer in Danish women. Acta Obstetricia et Gyne- cologica Scandinavica, 92, 1023–31.

Sah SK, González JV, Shrestha S, Adhikari A, Manandhar KD, Yadav SB, Stein DA, Gup-

ta BP, Picconi. MA. (2018). Human papillo- mavirus genotype distribution in cervical cancer biopsies from Nepalese women. In- fectious Agents and Cancer, 13(4), 1–7.

Tao G, Yaling G, Zhan G, Pu L, Miao. H.

(2018). Human papillomavirus genotype distribution among HPV‑positive women in Sichuan province, Southwest China.

Archieves Virology, 163, 65–72.

Walboomers JM, Jacobs MV, Manos MM, Bosch FX, Kummer JA, Shah KV, Snijders PJ, Peto J, Meijer CJ, Munoz N. (1999).

Human papillomavirus is a necessary cause of invasive cervical cancer world- wide. The Journal of Pathology, 189, 12–


WHO Global Cancer Report. (2003). Global cancer rates could increase by 50% to 15 million by 2020. Retrieved from http://



Medical Laboratory Technology Journal

Medical Laboratory Technology Journal

Available online at :

4 (2), 2018, 49-57

Comparison Analysis of Total Cholesterol Level Examination Between Photometry and 3 Parameters Point of Care Testing Device

*Perdina Nursidika, Wikan Mahargyani, Fitri Kurnia Anggraeni

Medical Laboratory Technology (D-4) , Jenderal Achmad Yani School of Health Sciences

*Email :

DOI: 10.31964/mltj.v%vi%i.184

Abstract: Total cholesterol is the composition of many substances including cholesterol, tri- glycerides, LDL cholesterol, and HDL cholesterol. Cholesterol examination is one of the most frequent tests required in the laboratory to monitor vascular and cardiovascular diseases. Most clinical pathology laboratories use photometer to perform clinical chemistry checks. Cholester- ol testing can also be done with Point of Care Testing (POCT) which has a working principle of biosensor technology. This research method is experimental, using 40 samples that can repre- sent normal and pathological levels. All samples will be checked for total cholesterol with a photometer of CHOD-PAP method and 3 POCT Lipid Pro. The results showed linear regres- sion y = 0.955x + 1.8325 with R2 of 0.9955. The linear regression value is calculated by Total Error (TE), while the Total Error Allowable (TEa) cholesterol is 10%. The bias value is 0.31%, TE for normal level = 5.92% and TE for high pathological level = 3.00%, it can be stated the re- sult of examination can be compared or accepted. The% TE value obtained is less than the TEa value of cholesterol. It can be concluded that the total cholesterol results examined by the photometer and LipidPro are comparable. For further research it is advisable to use a total cho- lesterol sample that has a value of more than 400 mg/dL.

Keywords: 3 Point of Care Testing (POCT); Cholesterol; Photometer

Received 2018-08-27; Revised 2018-10-08; Accepted 2018-12-16


Cholesterol, triglycerides and lipoproteins are important component of the composition of fat fractions in the human body (Dwizella, et al., 2018).

Cholesterol is unsaturated alcohol with steroid compounds. Cholesterol is essential for the function of animal cells and the basic constituent of cell membranes. Cholesterol is a precursor to various important compounds such as bile salts, adrenal steroid hormones and gonads. (Apro, 2015; Miller, 2013).

Triglycerides are esters of glycerol fatty acids and it represent the main lipid components of fat from food and animal fat deposits (Tsoupras, et al., 2018).

Cholesterol and triglycerides are insoluble non-polar compounds transported by plasma lipoproteins (Welty, 2013). Plasma lipoproteins are divided according to density, electrophoretic mobility, size, and content of cholesterol, triglycerides, and proteins (Pan &

Segrest, 2016). Lipoproteins are divided into five main classes, chylomicrons, very low

density lipoproteins (VLDL), intermediate- density lipoproteins (IDL), low-density lipoproteins (LDL), and high-density lipoproteins (HDL) (Sacks & Brewer, 2014).

Preliminary studies of cholesterol are a key component of arterial plaque which raises the cholesterol hypothesis in the pathogenesis of atherosclerosis (Buja, 2014; Steinberg, 2013). Population studies have shown that elevated LDL cholesterol levels (Zmysłowski &

Szterk, 2017) and apolipoprotein B (apoB) 100, The main structural protein LDL, is directly related to the risk of atherosclerotic cardiovascular events (ASCVE) (Ference et al., 2017). High triglyceride levels in the blood are one of the causes of atherosclerosis (Peng, Luo 2017). LDL peroxidation is the main key in the initiation and progression of atherosclerosis (Matsuura et al., 2014). Ather- osclerosis is a systemic disease (Hoshino et al., 2018). About 60% of patients with periph- eral artery disease will have ischemic heart disease, and 30% have cerebrovascular dis- ease.


Within five years of diagnosis, 10-15% pa- tients with intermittent claudication will die caused by cardiovascular disease. Therefore, treatment begins with identification and modifi- cation of common risk factors for peripheral artery disease, heart disease, and stroke (Frostegård, 2013; Morley et al., 2018).

Cholesterol examination is the most frequently requested tests in the laboratory to monitor vascular disease which includes coronary heart disease, cerebral vascular disease, and peripheral blood vessels (Sniderman et al., 2016). Most clinical patholo- gy laboratories use photometer devices to con- duct clinical chemistry examinations. This tool can determine the level of an ingredient in body fluids such as serum or plasma.

Photometer is a standard method of clinical chemistry examination but has several disadvantages, such as expensive prices, invasive blood sampling and relatively longer examination times (Frostegård, 2013; Morley et al., 2018). The length of the examination can cause prolonged results and a delay in di- agnosis. This will lead to services that are not suitable even fatal consequences can occur death. Continuously developed inspection technology is made to measure the inspection process, namely the Point of Care Testing (POCT) (Kost et al., 1999). Some countries like China have used POCT to reduce the number of people with dyslipidemia (Zhang et al., 2015).

Point of Care Testing (POCT) is a digital tool that uses cell measurements where cer- tain reactions can take place. This cell can be a porous matrix, chamber or surface. Measur- ing devices can be visual, optical or monitoring electrochemical reactions that occur. Generally POCT mechanism use biosensor technology.

Biosensor technology generated the electrical charge by chemical interactions between cer- tain substances in blood and chemicals in dry reagents (strips) and will be measured then converted into numbers that correspond to the amount of electric charge. The resulting num- ber is considered equal to the level of sub- stance measured in blood. The advantage of the POCT is that the results are fast so that the diagnosis can be immediately enforced and the action / treatment can be given imme- diately. In addition, this tool is easy to use, the sample volume used is less, the device is smaller so it does not need a special room and

can be carried or mobile. The disadvantage of this tool is that precision and accuracy are not good if compared to the reference method. In addition measurement capability is limited be- cause it is mediated by temperature, humidity, and hematocrit value (Kemenkes, 2010).

The use of POCT only takes less than 2 minutes to perform cholesterol and triglyceride tests (Xavier et al., 2016). This makes POCT suitable for disease screening tests (Ferreira et al., 2015). POCT can be used as a screening filter and diagnosis of hypercholesterolemia (Peverelle et al., 2018), and CVD risk assessment (OAM, 2016), and long-term monitoring of patients who have un- dergone treatment (Plüddemann et al., 2012).

POCT tools can be used by someone who does not have the basis of laboratory knowledge, they do not understand quality control over the results of POCT examination.

Based on Food and Drug Administration (FDA) data from the United States between 1984 and 1992, there were 24 deaths and 984 morbidity due to POCT use because of inappropriate testing. Errors in dealing with patients are usu- ally 50% due to incorrect instructions (indications), 32% fail to act because they are not in accordance with the test results (Kost et al., 1999).

POCT that is often used in hospital and clinic laboratories is LipidPro. The advantage of using this tool is faster inspection time and can reduce medical waste because 1 strip can directly check 3 parameters. Lipid Pro is a tool to check blood lipid levels in vitro which helps with practical and easy measurement of total cholesterol, HDL (High Density Lipoprotein), LDL (Low Density Lipoprotein), and triglycer- ides. The principle of this tool is to read the re- flection of light based on changes in the color of the results of the enzymatic reaction be- tween the substrate (total cholesterol, HDL cholesterol and triglycerides) and the enzymes in the strip. When the sample is dropped on the test strip, the sample will react and pro- duce the color that will be read by the tool. The intensity of the measured color is proportional to its concentration. The instrument compo- nent changes the resulting color to a numerical value and displays the value on the screen (Osang Healthcare, 2017).


Medical Laboratory Technology Journal MATERIAL AND METHOD

This research method is experimental.

The sample of the study was 40 patients who were examined for cholesterol levels in the Hospital in Bandung in the period February- March 2018. This research is approved by Jenderal Achmad Yani School of Sciences ethical committee declared by the ethical clearance number 013/KEPK/VIII/2018.

Before the examination, the patient had to fast beforehand to avoid the influence of the examination from the food. Specimens used are serum that is not hemolysis. The number of samples is 40 serum in accordance with Westgard provisions for comparability testing.

Cholesterol level examination using ELITech Selectra Pro-M and POCT Lipid Pro Photometers. The photometer used is capable of checking 266 tests / hour, can detect bar- codes, with a Quartz-Iodine light source 12V- 20W, absorbance photometric range of -0.1 to 3. ELITech Selectra Pro-M photometers using reagents have specifications can check cho- lesterol by range cholesterol levels of 20-600 mg / dL, measurements with the end point method, and 505nm wavelength. Cholesterol testing with POCT Lipidpro with the specifica- tions of one tool can check total cholesterol, triglycerides, and HDL-cholesterol, measure- ment time of 2 minutes, and have 200 data memory. LipidPro uses a kit with 5 unit choles- terol esterase enzyme specifications, 3.3 units of cholesterol oxidase, 3.3 units of peroxidase, 4-9 μg of Aminoanthipirin, and 81 μg of aniline derivate.

The material used in this study was normal control serum (CTN17L04 LOT) and pathological (No 01-1030 LOT), patient serum, Elitech Group cholesterol reagent, POCT LipidPro total cholesterol strip and tools.

Cholesterol examination with an ELItech Selectra Pro-M photometer performed an internal calibration before conducting the examination, followed by inserting 0.5-50 μL of serum into the cuvette, choosing a cholesterol check then clicking start (Elitech Group, 2018).

Cholesterol examination with LipidPro by in- serting the strip into the tool, entering the strip code on the tool, using the lancing device to take a blood sample and drop the sample on the strip. The results will be read after two minutes (Osang Healthcare, 2017).

The examination begins with internal quality stabilization with serum control checks.

After the data is on the target value and range

of examination, then it is followed by samples check. For normal control serum, the target value is 131 mg/dL with a range of 105-157 mg / dL. Pathological control series, the target value is 221 mg / dL with a range of 177-265 mg / dL. Data obtained from measurements of samples in the form of absorbance values for both Photometers and Lipids Pro. Data analy- sis technique uses linearity regression test cal- culation. Linear regression equation from Y to X is formulated shown by equation 1;


Y = dependent variable X = independent variable a = intercept

b = regression coefficient / slope

Then the bias calculation is performed.

Bias is the difference between the results of the serum control level and the actual value.

The bias value is processed into the total error value and the allowable (Tea) cholesterol total error value. Cholesterol TEa is 10%

(Westgard, 1992). Tea eaquation shown by

equation 2;


This research was conducted in one hospital in the Bandung with the parameter to- tal cholesterol levels. The number of samples used were 40 samples and were examined for 7 days, it measured by using POCT and Pho- tometer.Before measuring the patient sample, quality control (QC) performed by observing control serum during the examination period.

The results of the control serum examination show that a sample check can be performed.

The results of the examination of the normal control serum on the photometer are in table 1.




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