A Validated Register-Based Algorithm to Identify Patients Diagnosed with Recurrence of Malignant Melanoma in Denmark

Figures & data

Figure 1 Temporal overview of the algorithm to identify patients with recurrence of malignant melanoma. Reproduced from Cancer Epidemiol, 59, Rasmussen LA, Jensen H, Flytkjær Virgilsen L, Jellesmark Thorsen LB, Offersen BV, Vedsted P. A validated algorithm for register-based identification of patients with recurrence of breast cancer—Based on Danish Breast Cancer Group (DBCG) data, 129–134, Copyright (2019), with permission from Elsevier.¹⁵

Figure 2 Flowchart of the study population.

Table 1 Characteristics of Included Patients Stratified on Cancer Recurrence Status in the Gold Standard^a

Population Characteristics	Cancer Recurrence		No Cancer Recurrence
	n	(%)	n	(%)
N	95	(100)	1652	(100)
Sex
Female	39	(41)	872	(53)
Male	56	(59)	780	(47)
Age, median (IQI)	68 (59;76)		59 (45;70)
Tumor stage primary cancer
IA	15	(16)	982	(59)
IB	21	(22)	276	(17)
IIA	5	(5)	54	(3)
IIB	12	(13)	38	(2)
IIC	6	(6)	15	(1)
III–IV^b	5	(5)	6	(0.5)
Missing	31	(33)	281	(17)
Follow-up time^c, months (IQI)	31 (17;49)		55 (38;61)

Notes: ^aNumbers are n (%) if nothing else is stated. ^bStage IV is pooled with stage III to adhere to legislation on data privacy protection. ^cTime from primary melanoma surgery to the first of the following events: recurrence, emigration, death, or end of study (10 March 2018).

Abbreviation: IQI, interquartile interval.

Table 2 Concordance of Recurrence of Malignant Melanoma Identified by the Gold Standard and the Algorithm

		Recurrence by Gold Standard
		Yes	No	Total
Recurrence by algorithm	Yes	89	14	103
	No	6	1638	1644
	Total	95	1652	1747

Table 3 Performance of the Algorithm to Identify Recurrence from Malignant Melanoma (n=1747)

Algorithm Performance	All Indicators Combined	Indicators Stratified on Codes Related to Pathology, Procedure, and Diagnosis
	% (95% CI)	Pathology Codes^a % (95% CI)	Procedure Codes^b % (95% CI)	Diagnosis Codes^c % (95% CI)
Sensitivity	93.7 (86.8–97.6)	52.6 (42.1–63.0)	71.6 (61.4–80.4)	75.8 (65.9–84.0)
Specificity	99.2 (98.6–99.5)	99.7 (99.3–99.9)	99.4 (98.9–99.7)	99.5 (99.0–99.8)
PPV	86.4 (78.2–92.4)	90.9 (80.0–97.0)	87.2 (77.7–93.7)	90.0 (81.2–95.6)
NPV	99.6 (99.2–99.9)	97.3 (96.5–98.1)	98.4 (97.7–98.9)	97.6 (97.9–99.1)

Notes: ^aHistology similar to primary cancer histology and coded as recurrent, metastatic, or direct spread to surrounding tissue. ^bExcision of skin, excision of lymph nodes, amputation, immunotherapy, radiotherapy, and chemotherapy. ^cICD-10: C76-C79 (except C779), C43xM, C439X.

Abbreviations: CI, confidence interval; PPV, positive predictive value; NPV, negative predictive value.

Table 4 Accuracy of Cancer Recurrence Date as Estimated from the Algorithm Compared with the Gold Standard

Algorithm Estimated Date	% (95% CI)
Same date	23 (15–34)
Within 7 days	42 (32–54)
Within 14 days	57 (46–67)
Within 30 days	73 (64–82)
Within 60 days	84 (76–91)
Within 90 days	87 (79–93)

Rasmussen LA, Jensen H, Flytkjær Virgilsen L, Jellesmark Thorsen LB, Offersen BV, Vedsted P. A validated algorithm for register-based identification of patients with recurrence of breast cancer—Based on Danish Breast Cancer Group (DBCG) data. Cancer Epidemiol. 2019;59:129–134. doi:10.1016/j.canep.2019.01.01630743224

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