Observational study to calculate addictive risk to opioids: a validation study of a predictive algorithm to evaluate opioid use disorder

Figures & data

Table 1 Patient demographics

Population	n	W	B	H	A	M*	N*	D
Opioid dependent, n=258, mean age =36.9 years	Females, n=126 (49%)	109	6	5	3	0	1	2
	Males, n=123 (51%)	113	10	6	1	0	0	2
	Overall %	86.0	6.2	4.3	1.6	0	0.4	1.6
Control group, n=650, mean age =49.7 years	Females, n=418 (64%)	313	63	18	2	6	4	12
	Males, n=232 (36%)	172	29	21	1	0	0	9
	Overall %	74.6	14.2	6.0	0.5	0.9	0.6	3.2

Note:

* Races categorized as “others”, in statistical analyses.

Abbreviations: A, Asian/Pacific islander; B, black/African-American; D, declined; H, Hispanic; M, mixed; N, not listed; W, white/Caucasian.

Table 2 Proove Opioid Risk test panel markers

Protein name	Gene	SNP marker	Associated neuropsychiatric disorders
Catechol-O-methyltransferase	COMT	rs4680	Alcohol and drug abuse^Citation35,Citation36
			Anxiety^Citation37
			Depression^Citation38
Dopamine beta-hydroxylase	DBH	rs1611115	Cocaine addiction^Citation39,Citation40
			ADHD^Citation41
			Schizophrenia^Citation42
Dopamine D1 receptor	DRD1	rs4532	Depression^Citation43
			Heroin addiction^Citation44
Ankyrin repeat and kinase domain containing 1/dopamine receptor D2	ANKK1/DRD2	rs1800497	Alcohol and cocaine dependence^Citation20
Dopamine D4 receptor	DRD4	rs3758653	Anxiety^Citation21,Citation45
Dopamine transporter SLC6A3	DAT	rs27072	Methamphetamine addiction^Citation46
Gamma aminobutyric acid receptor A, gamma 2 subunit	GABRG2	rs211014	Alcohol abuse^Citation47
Opioid receptor, kappa 1	OPRK1	rs1051660	Mood disorders^Citation48
			Alcohol dependence^Citation49
Methylenetetrahydrofolate reductase	MTHFR	rs1801133	Bipolar disorder, depression^Citation50
Opioid receptor, Mu 1	OPRM1	rs1799971	Heroin addiction^Citation22
Serotonin receptor 2A	HTR2A	rs7997012	Drug abuse^Citation35
			Depression^Citation51

Abbreviations: ADHD, attention deficit hyperactivity disorder; SNP, single-nucleotide polymorphism.

Figure 1 Distribution of POR scores by OUD status.

Notes: OUD patients had significantly higher average POR scores compared to controls, P=7.97×10⁻³⁴. POR scores of OUD patients ranged from 8 to 46, with a mean of 17.6 (median =26, SD =5.07). POR scores of control patients ranged from 9 to 43, with a mean of 25.5 (median =17, SD =9.05).
Abbreviations: OUD, opioid use disorder; POR, Proove Opioid Risk; SD, standard deviation.

Figure 2 ROC curve of POR predicting OUD status.

Notes: At a POR score of ≥12, which is used to identify those at moderate and high risks, the POR algorithm has high sensitivity (95.7%) and low specificity (6.2%). At a POR score of ≥24, which is used to identify those at high risk, the sensitivity decreases to 55.4% and the specificity increases to 88.9%. The AUC of the ROC curve is 0.757 (95% confidence interval: 0.718–0.797), meaning that the POR algorithm is able to correctly diagnose OUD >75% of the time.
Abbreviations: AUC, area under the curve; OUD, opioid use disorder; POR, Proove Opioid Risk; ROC, receiver operating characteristic.

Figure 3 Performance of POR across different prevalence rates of OUD.

Notes: The consequences of increasing the POR score threshold to correctly identify OUD patients are decreased sensitivity and increased specificity. Conversely, decreasing the POR score threshold to correctly identify OUD patients results in an increased sensitivity and a decreased specificity. The POR performs equally well across different prevalence rates: 8% (population), 28% (cohort), and 50% (balanced).
Abbreviations: OUD, opioid use disorder; POR, Proove Opioid Risk.

Figure 4 ORs of OUD in each POR risk stratification.

Notes: Patients at moderate risk (POR score 12–23) had on average 4.17 increased odds of OUD. Patients at high risk (POR score ≥24) had on average 16.5 increased odds of OUD.
Abbreviations: OR, odds ratio; OUD, opioid use disorder; POR, Proove Opioid Risk.

Figure 5 PLRs and NLRs of OUD across different prevalence rates.

Notes: As the POR score increases, so does the PLR of OUD diagnosis, ie, the likelihood of correct OUD diagnosis given a minimum POR score. The NLR is the likelihood of correct diagnosis of no OUD given a minimum POR score. For all POR scores <42, the NLR is <1. The PLR and NLR are comparable across different prevalence rates: 8% (population), 28% (cohort), and 50% (balanced).
Abbreviations: NLRs, negative likelihood ratios; OUD, opioid use disorder; PLRs, positive likelihood ratios; POR, Proove Opioid Risk.

Table S1 Sensitivities and specificities of the POR algorithm using different cutoffs of POR scores to predict OUD

POR score ≥	Sensitivity (%)	Specificity (%)	POR score ≥	Sensitivity (%)	Specificity (%)
9	99.60	0.00	28	43.40	97.30
10	99.20	0.40	29	40.70	97.70
11	97.70	3.10	30	36.80	97.70
12	95.70	5.80	31	33.70	98.40
13	93.80	10.50	32	32.60	98.40
14	91.50	20.90	33	27.50	98.40
15	88.00	32.90	34	23.60	98.80
16	83.30	43.40	35	19.40	98.80
17	79.50	50.40	36	16.30	98.80
18	75.20	58.90	37	14.00	98.80
19	71.30	64.00	38	11.60	99.20
20	67.80	71.30	39	8.10	99.20
21	65.10	76.40	40	5.40	99.60
22	62.00	79.50	41	3.50	99.60
23	57.80	82.60	42	2.30	99.60
24	55.40	86.80	43	1.60	99.60
25	52.70	90.70	44	0.80	100.00
26	50.40	93.00	45	0.40	100.00
27	45.30	95.30

Abbreviations: OUD, opioid use disorder; POR, Proove Opioid Risk.

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