Performance comparison of CD4 T-cell count instruments based on data from the national proficiency testing program in China, 2016–2023

Abstract

Background

The number of CD4 T cells is an important indicator of immune status in people living with HIV (PWH). In China, numerous CD4 count instruments have been approved; however, no studies have been conducted to systematically compare the performance of these instruments.

Methods

We retrospectively analyzed data from the national proficiency testing program for CD4 T-cell counts in China from 2016 to 2023. We conducted comparative analysis to evaluate the performance of 10 mainstream CD4 count instruments. Passing-Bablok regression and mix-effects model analyses were used to analyze the agreement and interchangeability of test results among instruments.

Results

The proportion of laboratories participating in the CD4 proficiency testing program and using Chinese-manufactured and point-of-care testing instruments has increased annually, from 4.7% to 29.7% and from 3.3% to 8.1%, respectively. The results of the 10 devices tested showed strong correlations and agreement with target values, with correlation coefficients (R²) ranging from 0.952 to 0.983 and with the mean bias ranged from -1 to + 95 cells/µL across the 10 devices. Strong agreement was observed among the results for the 10 instruments, slopes ranged from 0.866 to 1.214, while the intercepts varied from  -42.3 cells/µL to + 24.0 cells/µL. After adjusting for sample and laboratory effects using a linear mixed-effects model, eight instruments (B, C, D, M, J, F_P, L_P, H) produced significantly lower values than the reference instrument A (adjusted mean = 613.0; differences: -92.0 to -20.3; all p < 0.05). Tukey-adjusted pairwise comparisons identified H, L_P, and F_P as a distinct low-value subgroup, although their measurements remained within the acceptable range.

Conclusions

The performance of commonly used CD4 count instruments was comparable and can meet the needs of HIV testing laboratories.

Clinical trial

Not applicable.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12879-026-13498-x.

Background

CD4 T lymphocytes are the primary targets of HIV infection, and they decrease progressively after infection. CD4 counts were previously used as a criterion for determining the optimal timing of initiating antiretroviral therapy (ART) in PWH [1, 2]. Since 2015, the World Health Organization (WHO) has recommended the rapid initiation of ART for individuals diagnosed with HIV infection, regardless of the level of CD4 counts [3]. However, obtaining precise CD4 T-cell counts remains crucial for understanding the immune status and disease progression of patients and determining prognosis and the effectiveness of ART [4].

Proficiency testing (PT) programs facilitate the identification of deficiencies in the laboratory testing process and subsequent enhancement of the laboratory’s testing capabilities [5]. To ensure the quality of CD4 testing laboratories in China, the National AIDS Reference Laboratory (NARL) has administered the national CD4 PT program every year since 2004. From 2016 to 2023, the number of CD4 testing laboratories in China increased from 1,230 to 1,349. Over the past 8 years, an average of 27.8% of CD4 testing laboratories have participated in the NARL CD4 PT program each year, among which 87.8% belong to the Centers for Disease Control and Prevention (CDCs), covering all 31 provincial CDCs as well as major prefectural and municipal CDCs in China. Annually, more than 97.5% of Chinese HIV testing laboratories enrolled in CD4 T-cell proficiency testing attained a “Satisfying” performance rating.

HIV testing laboratories in China previously relied on imported flow cytometers for CD4 counting. However, the high testing costs and complex testing procedures associated with imported flow cytometers have hindered widespread implementation of CD4 T-cell testing services in some rural areas [6]. The recent development of Chinese-manufactured instruments has provided more options for HIV testing laboratories. Additionally, the development of point-of-care testing (POCT) technology has expanded the accessibility of CD4 counting services across the country [6]. Given the diversity of CD4 count instruments on the market in China, it is crucial to analyze and compare their performance to understand whether differences in performance could affect CD4 counting results, so as to provide high-quality testing services nationwide. However, previous studies have usually compared the performance of only two or three instruments, with most studies focusing on evaluating the performance of POCT instruments [7–9]. The performance of common CD4 count instruments on the Chinese market has not been systematically analyzed. In our study, we retrospectively analyzed results from the NARL CD4 PT program during 2016–2023 in the first systematic comparison of testing performance of all CD4 count instruments commonly used in China. The aim of this study was to provide reference data for HIV testing laboratories to select appropriate CD4 count instruments in practice.

Materials and methods

Specimen distribution and testing

Two stabilized whole blood specimens (one low value and one normal value) were distributed annually to CD4 PT laboratories by the NARL. These specimens were commercial stabilized whole blood cells purchased from Synexa Life Sciences (South Africa, 2016–2021) and Beckman Coulter (USA, 2022–2023), respectively, and were intended for use as reference controls; detailed target values, expected ranges, batch numbers, and other details are shown in Supplementary Table 1. According to the product instructions, the source materials of the controlled products are HIV, HBV, HCV negative, however, no test method can offer complete assurance that HIV, HBV, HCV or other infectious agents are absent, these specimens are still considered potentially infectious. Additionally, stabilized whole blood specimens from Synexa Life Sciences remain stable until their labeled expiry date—whether opened or unopened—when stored at 2–8 °C, as stated in the manufacturer’s instructions. In contrast, specimens from Beckman Coulter are stable for up to 90 days after opening under the same storage conditions, according to the manufacturer’s product insert or instructions for use. The specimens are dispatched in a foam box with cold packs to maintain a temperature of approximately 4℃ throughout the shipping process by express delivery, ensuring that their quality is preserved. Additionally, the specimens are enclosed in airtight plastic packaging with appropriate sorbent materials to absorb any leaks in the event of a rupture. Upon receipt of specimens, laboratories should proceed with analysis in accordance with the laboratory’s standard operating procedure for testing patient samples without dilution; the test results must be returned to the NARL within three weeks. From 2016 to 2023, the NARL distributed PT specimens to a total of 2,874 laboratories and obtained 5,748 test results (The distribution of laboratory test results is shown in Figure S1. and Supplementary Tables 6 − 1, Supplementary 6 − 2).

Labeling of CD4 count instruments in the NARL CD4 PT program

In this study, letters were adopted to substitute the names of instruments used by laboratories participating in the NARL CD4 PT program. The pairing between letters and instruments was A: FACSCalibur(Becton Dickinson, America), B: FACSCount(Becton Dickinson, America), C: FACSCanto(Becton Dickinson, America), D: FACSVia(Becton Dickinson, America), E: FACSLyric(Becton Dickinson, America), F: FACSPresto(Becton Dickinson, America), G: EpicsXL(Beckman Coulter, America), H: FC500(Beckman Coulter, America), I: Navios(Beckman Coulter, America), J: DxFLEX(Beckman Coulter, America), K: CyFlow(Partec, Germany), L: Pima(Abbott, America), M: NovoCyte(Agilent, China), N: BriCyteE6(Mindray, China), O: Cytonova2040(CaptialBio, China), P: Sparrow(Celula, China), Q: EasyCell(Wellgrow, China), R: ZS-AE7S(Biosino, China), S: BeamCyte 1026(BeamDiag, China), and T: RaiseCyte2L6C(RaiseCare, China).

After analyzing the proportion of each instrument used by laboratories participating in the CD4 PT program from 2021 to 2023 (The number and proportion of laboratories using relevant equipment see Supplementary Table 2), the 10 most commonly used devices were selected as the CD4 count instruments evaluated in this study; these were A, B, C, D, F, H, J, L, M, and N, information on the ten major devices is provided in Supplementary Table 3.

Statistical analysis

In this study, we grouped laboratories based on the testing platforms they utilized. The value of a PT specimen measured with a certain instrument was determined as the median of the test results reported by all laboratories using the instrument. Ordinary least squares regression and Bland-Altman analysis was used to analyze the relationship and agreement between test results from the 10 most commonly used instruments and the target values. Although the target values were manufacturer-assigned, they were derived from multi-device measurements and thus carry some uncertainty; Outlier detection identified the 2020 Normal PT specimen as a statistical outlier (Grubbs’ test), which was excluded from primary analyses to prevent undue influence on bias and agreement estimates. Passing-Bablok regression and mixed effects models analysis were performed to analyze the agreement and interchangeability of CD4 count measurements across 10 analytical platforms. In accordance with the Expert Consensus on Quality Management in Flow Cytometry for Clinical Laboratories [10], the relative or absolute bias of results from comparison instruments should not exceed 20%. Microsoft Excel 2019 (Microsoft Corporation, Redmond, WA, USA) was used for data collection. Statistical analysis and graphing were performed using GraphPad Prism version 9.5.0 (GraphPad Software, San Diego, CA, USA) and R version 4.5.1 (R Foundation for Statistical Computing, Vienna, Austria).

Results

Changes in instruments used by participating laboratories

The number of instrument types used in a single year increased from 10 in 2016 to 17 in 2023 (Fig. 1). The different types of imported instruments rose from 8 in 2016 to 10 in 2023, while the variety of Chinese-manufactured instruments grew from 2 in 2016 to 7 in 2023. Additionally, the number of POCT instruments increased from 1 in 2016 to 2 in 2023. Over the entire 8-year period from 2016 to 2023, a total of 20 distinct instrument models were employed, including 12 distinct imported models and 8 distinct Chinese-manufactured models, as some models were replaced or rotated in different years. Among these instruments, A, B, C, E, G, H, I, and J utilize bead-based principles for CD4 counts; instruments D, K, M, N, O, P, Q, R, S, and T employ volumetric principles for CD4 counts; and instruments F and L are POCT devices (denoted F_P and L_P). Detailed information on the equipment used annually can be found in Supplementary Table 4.

Fig. 1.

Proportion of participating laboratories using certain instruments in the NARL CD4 T-cell PT programs, 2016–2023. Note: Subscript P indicates POCT instrument

Although imported instruments were still the most commonly used in laboratories participating in the NARL CD4 PT program, the proportion of such instruments has been on a steady decline, from 95.3% in 2016 to 70.3% in 2023. In contrast, the proportion of laboratories using Chinese-manufactured instruments increased from 4.7% to 29.7%, and the proportion of laboratories using POCT instruments increased from 3.3% to 9.5%.

Strong correlations between test results of 10 instruments and target values

Ordinary least squares regression analyses revealed strongly significant linear correlations between test results for the 10 devices evaluated and the target values, with correlation coefficients (R²) ranging from 0.952 to 0.983 (Fig. 2). The highest R² value 0.983 was observed between the most widely used imported instrument D and the targets; the lowest R² (0.952) was found between the POCT instrument F_P and the targets. The R² between the targets and another widely used POCT instrument L_P was 0.963. The R² values between the Chinese-manufactured instruments M and N and target values were 0.967 and 0.979, respectively.

Fig. 2.

Correlation analyses between the test results of 10 devices and target values in the NARL CD4 T-cell count PT program, 2016–2023. Note: Each blue dot represents test results for a PT specimen; solid line represents the fitted linear trend. R² and conversion formulas for the correlation analysis of the test results for the 10 devices tested and target values are labeled on each graph

Strong agreement between test results of 10 instruments and target values

The agreement between the CD4 count measurements from each of the 10 instruments and the target values was assessed using Bland-Altman analysis (Table 1, Fig. S2). Analyses were performed after excluding the 2020 outlier. The mean bias, defined as the average difference between the instrument test results and the target values, ranged from -1 to + 95 cells/µL across the 10 devices. The smallest absolute mean bias of 1 cells/µL was observed for the instrument J, while the largest absolute mean bias of 95 cells/µL was found for the Chinese-manufactured instrument N.

Table 1.

Bland–Altman analysis for agreement between CD4 T cell count measurements from 10 instruments and target values in the NARL CD4 T cell count PT program, 2016–2023

Method	N	Bias	LoA_param_lower	LoA_param_upper	LoA_nonparam _lower 2.50%	LoA_nonparam_upper 97.50%	shapiro_p
A vs. Target	15	+ 58	-50	+ 165	-		0.1286
B vs. Target	15	+ 50	-60	+ 159			0.4922
C vs. Target	15	+ 51	-69	+ 171			0.1429
D vs. Target	7	+ 19	-53	+ 91			0.1722
FP vs. Target	7	-29	-127	+ 70			0.0549
H vs. Target	15	-12	-108	+ 84	-142	+ 41	0.0276
J vs. Target	13	-1	-107	+ 106			0.0707
LP vs. Target	15	-16	-119	+ 88			0.0958
M vs. Target	15	+ 29	-80	+ 139			0.3127
N vs. Target	15	+ 95	-28	+ 218			0.8577

Agreement analysis between test results for 10 CD4 count instruments using Passing-Bablok regression

The Passing-Bablok regression analysis comparing CD4 counts across 10 different instruments demonstrated generally good agreement(Table 2, Fig. S3). The slopes ranged from 0.866 (B vs. F_P) to 1.214 (H vs. N), while the intercepts varied from  -42.3 cells/µL (H vs. L_P) to + 24.0 cells/µL (L_P vs. N). However, pairs involving instrument N (e.g., J vs. N, L_P vs. N, M vs. N) consistently showed slopes greater than 1.1, indicating that instrument N systematically reports higher CD4 counts compared to other platforms.

Table 2.

Passing-Bablok regression analysis of 10 CD4 instruments in the NARL PT program, 2016–2023

Comparison	Method1	Method2	Intercept	Slope	Sample Size
A vs. B	A	B	-19.4	1.013	16
A vs. C	A	C	-7.6	1.003	16
A vs. D	A	D	-4.4	0.957	8
A vs. FP	A	FP	-28.2	0.921	8
A vs. H	A	H	-2.8	0.903	16
A vs. J	A	J	-28.6	0.958	14
A vs. LP	A	LP	-31.0	0.920	16
A vs. M	A	M	-14.2	0.978	16
A vs. N	A	N	-13.0	1.090	16
B vs. C	B	C	3.5	0.997	16
B vs. D	B	D	10.1	0.938	8
B vs. FP	B	FP	-10.3	0.866	8
B vs. H	B	H	13.3	0.889	16
B vs. J	B	J	2.8	0.916	14
B vs. LP	B	LP	-14.0	0.903	16
B vs. M	B	M	0.4	0.972	16
B vs. N	B	N	1.9	1.080	16
C vs. D	C	D	-1.4	0.982	8
C vs. FP	C	FP	-19.7	0.903	8
C vs. H	C	H	7.2	0.902	16
C vs. J	C	J	-2.5	0.944	14
C vs. LP	C	LP	-8.6	0.902	16
C vs. M	C	M	-2.5	0.965	16
C vs. N	C	N	-2.4	1.077	16
D vs. FP	D	FP	-23.7	0.933	8
D vs. H	D	H	-16.1	0.980	8
D vs. J	D	J	2.4	0.947	8
D vs. LP	D	LP	-24.1	0.971	8
D vs. M	D	M	-8.3	1.026	8
D vs. N	D	N	-1.8	1.092	8
FP vs. H	FP	H	5.7	1.069	8
FP vs. J	FP	J	20.5	1.039	8
FP vs. LP	FP	LP	8.0	1.030	8
FP vs. M	FP	M	14.1	1.060	8
FP vs. N	FP	N	16.6	1.176	8
H vs. J	H	J	-12.8	1.030	14
H vs. LP	H	LP	-42.3	1.048	16
H vs. M	H	M	-13.9	1.089	16
H vs. N	H	N	-13.3	1.214	16
J vs. LP	J	LP	-14.4	0.976	14
J vs. M	J	M	-5.7	1.055	14
J vs. N	J	N	1.9	1.124	14
LP vs. M	LP	M	10.6	1.074	16
LP vs. N	LP	N	24.0	1.174	16
M vs. N	M	N	-10.5	1.127	16

Comparison of test results for 10 CD4 count instruments using a linear mixed-effects model

A linear mixed-effects model was used to compare measurement values across 10 devices, accounting for the non-independence of repeated measurements by including random intercepts for both Sample (n = 16) and Laboratory (n = 765). The variance components estimates indicated that 94.8% of the total variability was attributable to differences between samples, 0.8% to between-laboratory variation, and 4.3% to residual error. After adjusting for these sources of variation, differences among instruments were observed (Fig. 3, Supplementary Table 5). Instrument A served as the reference, with an adjusted mean of 613.0. Instrument N exhibited the highest adjusted mean (623.9), although this difference was not statistically significant (p = 0.072). In contrast, instruments B, C, D, M, J, F_P, L_P, and H all produced significantly lower values than instrument A, with differences ranging from  -92.0 to -20.3 (all p < 0.05).

Fig. 3.

Adjusted mean by method with 95% confidence intervals (error bars plot). Note: Points represent marginal means estimated from the linear mixed-effects model; error bars indicate 95% confidence intervals. Non-overlapping confidence intervals suggest statistically significant differences

Post hoc pairwise comparisons with Tukey adjustment confirmed that instruments H, L_P, and F_P formed a distinct low value group (Table 3), showing no significant differences among them but being significantly lower than instruments A and N. Notably, instrument N was significantly higher than instruments B, D, F_P, H, J, L_P, and M (all p < 0.05), suggesting a potential systematic upward bias.

Table 3.

Pairwise comparisons between different devices (Tukey-adjusted)

Contrast	Estimate	SE	z.ratio	p.value
A-B	20.341	3.301	6.163	0.000
A-C	10.303	4.935	2.088	0.536
A-D	37.787	10.704	3.530	0.015
A-FP	76.775	11.555	6.644	0.000
A-H	92.005	5.716	16.096	0.000
A-J	41.484	6.617	6.269	0.000
A-LP	83.120	5.729	14.509	0.000
A-M	31.362	4.171	7.519	0.000
A-N	-10.851	6.035	-1.798	0.737
B-C	-10.038	5.525	-1.817	0.725
B-D	17.446	10.947	1.594	0.852
B-FP	56.434	11.805	4.780	0.000
B-H	71.664	6.082	11.782	0.000
B-J	21.143	7.023	3.011	0.078
B-LP	62.778	6.128	10.245	0.000
B-M	11.021	4.702	2.344	0.361
B-N	-31.193	6.432	-4.850	0.000
C-D	27.484	11.449	2.401	0.325
C-FP	66.472	12.244	5.429	0.000
C-H	81.702	7.094	11.517	0.000
C-J	31.181	7.793	4.001	0.003
C-LP	72.817	7.100	10.256	0.000
C-M	21.059	5.898	3.571	0.013
C-N	-21.155	7.365	-2.872	0.113
D-FP	38.988	15.347	2.540	0.247
D-H	54.218	11.804	4.593	0.000
D-J	3.697	12.117	0.305	1.000
D-LP	45.333	11.822	3.835	0.005
D-M	-6.424	11.111	-0.578	1.000
D-N	-48.638	11.841	-4.108	0.002
FP-H	15.230	12.627	1.206	0.972
FP-J	-35.291	12.884	-2.739	0.158
FP-LP	6.345	12.475	0.509	1.000
FP-M	-45.413	11.903	-3.815	0.005
FP-N	-87.626	12.642	-6.931	0.000
H-J	-50.521	8.226	-6.142	0.000
H-LP	-8.885	7.635	-1.164	0.978
H-M	-60.643	6.623	-9.156	0.000
H-N	-102.857	7.894	-13.030	0.000
J-LP	41.636	8.329	4.999	0.000
J-M	-10.122	7.311	-1.384	0.932
J-N	-52.336	8.437	-6.203	0.000
LP-M	-51.757	6.587	-7.857	0.000
LP-N	-93.971	7.906	-11.885	0.000
M-N	-42.214	6.826	-6.184	0.000

Discussion

In this study, we identified strong correlations and agreement among test results of the 10 CD4 count instruments most commonly used in China and target values provided by the manufacturers. Our study revealed that the two most widely used Chinese-manufactured instruments and two POCT instruments showed good agreement with imported instruments in testing performance.

The PT specimens used in this study were commercially available stabilized whole blood controls. While laboratory qualification in the PT program was evaluated using group consensus means, the target values used for this study’s analysis were those assigned by the manufacturer of the quality control material. In 2020, the normal value specimen detection value was below the target value; the detection values of the other specimens fell within the expected target range. We suspect that this discrepancy may result from improper storage or transportation conditions of the commercial specimens, leading to sample degradation or alteration. Alternatively, the difference could be owing to the receipt of two identical low value control samples. To verify whether the measured values accurately reflect the true or expected results—specifically, accuracy of the measurements—we conducted both correlation and agreement analyses between the measured values of the 10 tested devices and the target values. The correlation analysis revealed coefficients of determination (R²) ranging from 0.952 to 0.983, reflecting varying levels of random error (precision) across platforms. Regression slopes ranged from 0.94 to 1.15, with 90% of platforms meeting the acceptable criterion of 0.90–1.10 for CD4 assays, indicating acceptable proportional error. However, the Chinese-manufactured platform N (slope = 1.15) exhibited overestimation, suggesting the need for improved calibration and standardization. The Bland-Altman analysis indicated that the mean bias between instrument measurements and target values ranged from -1 to + 95 cells/µL. This observation aligns with our overall findings, suggesting high measurement accuracy and agreement between the measured values of the 10 tested devices and the target values. This analysis helps in understanding the relationship between measured values and the expected target values, allowing assessment of the accuracy of the measurement process.

For many years, CD4 T-cell counting for PWH in China relied on imported flow cytometers, but the high cost of these imported instruments made this testing service challenging in some resource-limited areas [6]. In recent years, with the development of Chinese-manufactured flow cytometry technology, the number and types of available Chinese-manufactured CD4 count instruments has increased. According to the latest data from the FDA, the number of Chinese-manufactured flow cytometer manufacturers has risen to 29 [11]. Most Chinese-manufactured CD4 count instruments reduce the cost per test using the volumetric method [12]. This method has been proven to be a simple and reliable approach that can substitute bead-based procedures [13]. In this study, the two most widely used Chinese-manufactured instruments were comparable and their performance was in good agreement with imported instruments with slopes ranged from 0.965 to 1.174, while the intercepts varied from 0.4 cells/µL to 24.0 cells/µL. Mixed-effects model analysis revealed that instrument M differed significantly from instruments A, C, Fp, H, and Lp with absolute differences ranging from 6 to 60. Instrument N did not differ significantly from instruments A and C but showed statistically significant differences from all other instruments, with absolute differences ranging from 21 to 102. However, since the relative differences among all these instruments remained within 20%, which is acceptable. These findings indicate that Chinese-manufactured instruments can be a reliable alternative to imported instruments for CD4 T-cell counting in resource-limited areas.

POCT technology has been developed and applied to CD4 count testing in recent years. POCT CD4 count instruments provide effective solutions for grassroots laboratories because of their convenient operation and low equipment and training requirements [14]. Previous studies have demonstrated a high correlation between the results of POCT instruments and conventional flow cytometry. POCT instruments have also been recommended for use in some resource-limited areas [15, 16]. Although good aggrement were found between POCT instruments and conventional laboratory instruments in this study, mixed-effects model results indicated the two POCT instruments were slightly lower than the representative laboratory instrument A, with absolute differences of 76 cells/µL and 83 cells/µL, respectively. Previous studies have also shown that POCT CD4 count instruments yield lower counts than laboratory instruments, with absolute mean bias values ranging from 20 to 80 cells/µL [8, 9, 17]. Although discrepancies between POCT and laboratory-based instrument results may arise from differences in underlying testing principles or operational procedures, such differences are generally regarded as acceptable [10].

Our study data are derived from PT programs, which are fundamentally designed to monitor laboratory proficiency rather than serve as a validation tool for devices. Consequently, Several methodological limitations arising from this design must be carefully considered when interpreting our findings. The PT data were collected from multiple laboratories across different geographic regions, and interlaboratory variation, including differences in operator training and technical competence, environmental conditions, instrument maintenance, and quality control practices may have introduced additional error beyond the inherent analytical performance of each platform, making it challenging to disentangle platform-specific effects from laboratory-specific operational factors that could influence measured CD4 counts. Furthermore, our analysis incorporated PT specimens tested across multiple time periods spanning from 2016 to 2023, and temporal variation during this extended study period may have added additional sources of error to the analysis; specifically, changes in reagent lots, instrument calibration, or even turnover of laboratory personnel over time could have systematically influenced assay performance in ways that our cross-sectional analytical approach may not fully capture or adjust for. Another important consideration relates to the commutability of the PT specimens: the samples were commercially available stabilized whole blood controls rather than fresh clinical specimens, and these stabilized preparations may differ from native clinical samples in terms of matrix composition and presence of preservatives, potentially affecting assay performance in platform-specific ways; consequently, the biases observed in our analysis may not fully reflect performance during routine clinical testing with patient-derived whole blood samples. Additionally, the limited number of PT samples per instrument constrained our statistical power, particularly for newly marketed devices, potentially introducing bias in performance comparisons. This limitation necessitated focusing on 10 CD4 counting instruments, leaving the performance of newer or less widely used platforms insufficiently characterized in real-world settings.

Conclusions

Although the different types of CD4 count instruments used in HIV testing laboratories in China have increased, both imported and Chinese-manufactured instruments demonstrate good agreement and appear to provide comparable testing results, which can meet the needs for CD4 T-cell detection in HIV testing laboratories throughout China.

Supplementary Information

Below is the link to the electronic supplementary material.

Abbreviations

PWH

People Living with HIV

ART

Antiretroviral Therapy

WHO

World Health Organization

PT

Proficiency Testing

NARL

National AIDS Reference Laboratory

CDC

Centers for Disease Control and Prevention

POCT

Point-of-care Testing

Author contributions

CJ: Conceptualization; HZ, HFD and QYZ: methodology; MZN and HFD: software; HZ, HFD, QYZ, MZN, XZ, MJD and FFL: formal analysis; HZ, HFD, PL and MLW: writing—original draft preparation; CJ: writing—review and editing. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the Beijing Natural Science Foundation [grant no. L234054 and no. L244069 and the project from the National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases.

Data availability

The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.

Declarations

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Disclaimer

The findings and conclusions in this report are those of the authors and do not necessarily represent the views of the Centers for Disease Control and Prevention.

Competing interests

The authors declare no competing interests.