Detection of Pelvic Inflammatory Disease: Development of an Automated Case-Finding Algorithm Using Administrative Data

Table 3

Performance statistics for PID case identification using an algorithm applied to sample administrative data from women with an ICD-9 code* related to PID.

(a) Accuracy of PID case-finding algorithm: GH development dataset.

		New algorithm classification		Total
		Not PID	PID
Chart-confirmed Diagnosis	Not PID	34	40	74
	PID	10	265	275
	Total	44	305	349

(b) Accuracy of PID case-finding algorithm: KPCO validation dataset.

		New algorithm classification		Total
		Not PID	PID
Chart-confirmed Diagnosis	Not PID	34	58	92
	PID	34	315	349
	Total	68	373	441

(c)

Performance Statistics (95% CI)	GH development dataset	KPCO validation dataset
PID case identification using ICD-9 codes* alone
PPV	78.8% (74.1–83.0%)	79.1% (75.0–82.8%)
PID case identification using algorithm
Sensitivity	96.4% (93.4–98.2%)	90.3% (86.7–93.2%)
Specificity	45.9% (34.3–57.9%)	37.0% (27.1–47.7%)
PPV	86.9% (82.6–90.5%)	84.5% (80.4–88.0%)
NPV	77.3% (62.2–88.5%)	50.0% (37.6–62.4%)
Proportion of potential cases misclassified	14.3% (10.8–18.5%)	20.9% (17.2–25.0%)

*ICD-9 codes shown in Table 1. Only potential cases with complete chart-review information are included.
GH: Group Health, KPCO: Kaiser Permanente Colorado, PID: pelvic inflammatory disease, CI: confidence interval, PPV: positive predictive value, NPV: negative predictive value.