Performance of COPD population screener questionnaire in COPD screening: a validation study and meta-analysis

Figures & data

Table 1. Clinical characteristics of included subjects.

	COPD	Control	p Value
Number	157	840
Age （year）	64.75 ± 10.48	58.06 ± 11.68	<.001
Gender
Male	112	452	<.001
Female	45	388
BMI	22.36 ± 3.55	23.58 ± 3.55	<.001
Lung function test
FEV1 (L)	1.56 ± 0.67	2.45 ± 0.71	<.001
FVC (L)	2.63 ± 0.89	3.09 ± 0.86	<.001
FEV1/FVC (%)	57.71 ± 8.86	79.56 ± 5.46	<.001
FEV1 Pred%	66.76 ± 22.12	99.51 ± 17.26	<.001
Smoking status
Never smoker	51	440	<.001
Ever smoker	39	113
Current smoker	67	288
GOLD stage
I	45	—
II	75	—
III	27	—
IV	10	—
COPD-PS score	5.03 ± 2.11	2.72 ± 1.80	<.001

COPD-PS: chronic obstructive pulmonary disease population screener; FEV₁: forced expiratory volume in the first second; FVC: forced vital capacity; GOLD: global initiative for chronic obstructive lung disease.

Figure 1. Receiver operating characteristic (ROC) curve of the use of COPD-PS to discriminate between COPD patients and controls. COPD-PS: chronic obstructive pulmonary disease population screener.

Table 2. Performance of COPD-PS in detecting COPD with various cut-off values.

Cut-off value	Sensitivity (%)	Specificity (%)	Positive predictive value (%)	Negative predictive value (%)
3	85.99	51.67	24.95	95.18
4	74.52	70.24	31.88	93.65
5	54.78	86.43	43.00	91.09
6	43.31	92.38	51.52	89.71
7	26.11	96.43	57.75	87.47

Figure 2. Quality assessment of studies on COPD-PS. COPD-PS: chronic obstructive pulmonary disease population screener.

Table 3. Clinical summary of included studies.

Author	Study number	Year	Country	COPD	GOLD (I/II/III/IV)	Control	Source of subjects	Age requirement	COPD definition	Cut-off	TP	FP	FN	TN
Martinez et al. [9]	1	2008	USA	113	NA	182	GP/SS	≥35	Spirometry	6	83	48	30	134
Tsukuya et al. [10]	2	2015	Japan	153	71/73/8/1	2204	General population	40–79	Spirometry	4	103	597	50	1607
Sogbetun et al. [20]	3	2016	USA	101	NA	261	Primary care clinics	NA	Spirometry	5	74	144	27	117
Kobayashi et al. [21]	4	2017	Japan	27	NA	46	Primary care clinics and hospitals	≥40	Spirometry	5	17	15	10	31
Llordés et al. [22]	5	2017	Spain	107	NA	300	General population	≥45	Spirometry	4	86	157	21	143
Spyratos et al. [23]	6	2017	Greece	351	NA	2883	General population	>40	Spirometry	5	196	288	155	2595
Soriano et al. [24]	7	2018	Spain	130	NA	6839	Primary care centres	≥35	Spirometry	4	108	546	22	6293
Gu et al. (current study)	8	—	China	157	45/75/27/10	840	Subjects underwent health examination	≥40	Spirometry	4	117	250	40	590

COPD: chronic obstructive pulmonary disease; FN: false negative; FP: false positive; GOLD: Global Initiative for Chronic Obstructive Lung Disease; TN: true negative; TP: true positive; NA: not available.

Figure 3. Forest plot of sensitivity COPD-PS with random-effects model. The point estimates of sensitivity from each study are shown as solid circles. Error bars indicate 95% CIs. COPD-PS: chronic obstructive pulmonary disease population screener.

Figure 4. Forest plot of specificity of COPD-PS with random-effects model. The point estimates of specificity from each study are shown as solid circles. Error bars indicate 95% CIs. COPD-PS: chronic obstructive pulmonary disease population screener.

Figure 5. Forest plot of diagnostic odds ratio of COPD-PS with random effects model. The point estimates of specificity from each study are shown as solid circles. Error bars indicate 95% CIs. COPD-PS: chronic obstructive pulmonary disease population screener.

Figure 6. Summary receiver operating characteristic (SROC) curve of COPD-PS as a screening tool for COPD. COPD-PS: chronic obstructive pulmonary disease population screener. Q: The maximum joint value of sensitivity and specificity of COPD-PS.

Figure 7. Deeks’ funnel plot assessing the likelihood of publication bias. The statistically nonsignificant p-value of 0.37 for the slope coefficient suggests symmetry in the data and a low likelihood of publication bias. ESS: effective sample size, corresponding to 4 × Ncontrol × N_COPD/(Ncontrol + N_COPD).

Martinez FJ, Raczek AE, Seifer FD, et al. Development and initial validation of a self-scored COPD Population Screener Questionnaire (COPD-PS). COPD. 2008;5(2):85–95.

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Tsukuya G, Matsumoto K, Fukuyama S, et al. Validation of a COPD screening questionnaire and establishment of diagnostic cut-points in a Japanese general population: the Hisayama study. Allergol Int. 2015;64(1):49–53.

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Spyratos D, Haidich AB, Chloros D, et al. Comparison of three screening questionnaires for chronic obstructive pulmonary disease in the primary care. Respiration. 2017;93(2):83–89.

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Soriano JB, Molina J, Miravitlles M. Combining case-finding methods for COPD in primary care: a large, two-stage design study. Int J Tuberc Lung Dis. 2018;22(1):106–111.

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Data availability statement

All data used to support the findings of this study are available from the corresponding authors upon reasonable request.