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. 2022 Jan 7;4(1):20–31. doi: 10.37737/ace.22004

Table 5. Validity of case-finding algorithms for serious infection in the MDV administrative healthcare databasea.

Case-finding algorithmb Case definitions True cases Cases meeting definition True positive cases Pseudosensitivity PPV
(a + c)c (a + b)d (a)e a/(a + c)f 95% CI a/(a + b) 95% CI
1 BC 1 AND AC 1 167 195 167 100 (167/167) 100–100 85.6 (167/195) 80.7–90.6
2 BC 1 AND AC 2 167 52 41 24.6 (41/167) 18.0–31.1 78.9 (41/52) 67.8–90.0
3 BC 1 AND AC 3 167 187 162 97.0 (162/167) 94.4–99.6 86.6 (162/187) 81.8–91.5
4 BC 1 AND AC 4 167 166 140 83.8 (140/167) 78.3–89.4 84.3 (140/166) 78.8–89.9
5 BC 1 AND AC 5 167 158 140 83.8 (140/167) 78.3–89.4 88.6 (140/158) 83.7–93.6
6 BC 1 AND AC 6 167 1 1 0.6 (1/167) 0.0–1.8 100 (1/1) 100–100
7 BC 1 AND (AC 1 OR 2) 167 195 167 100 (167/167) 100–100 85.6 (167/195) 80.7–90.6
8 BC 1 AND (AC 1 OR 2 OR 3) 167 196 167 100 (167/167) 100–100 85.2 (167/196) 80.2–90.2
9 BC 1 AND (AC 1 OR 2 OR 3 OR 4) 167 200 167 100 (167/167) 100–100 83.5 (167/200) 78.4–88.6
10 BC 1 AND (AC 1 OR 2 OR 3 OR 4 OR 5) 167 200 167 100 (167/167) 100–100 83.5 (167/200) 78.4–88.6
11 BC 1 AND (AC 1 OR 2 OR 3 OR 4 OR 5 OR 6) 167 200 167 100 (167/167) 100–100 83.5 (167/200) 78.4–88.6
12 BC 1 AND (AC 1 OR 4 OR 5) 167 200 167 100 (167/167) 100–100 83.5 (167/200) 78.4–88.6
13 BC 1 AND (AC 1 OR 4 OR 5 OR 6) 167 200 167 100 (167/167) 100–100 83.5 (167/200) 78.4–88.6
14 BC 1 AND (AC 1 OR 2 OR 4 OR 5 OR 6) 167 200 167 100 (167/167) 100–100 83.5 (167/200) 78.4–88.6
15 BC 1 AND (AC 4 OR 5 OR 6) 167 188 159 95.2 (159/167) 92.0–98.5 84.6 (159/188) 79.4–89.7

Bold = case-finding algorithms identified as best fit based on a balance of PPV and pseudosensitivity.

a A random sample (n = 200) or 3,559 potential cases were analyzed.

b Serious infection basic and additional conditions are defined in Table 2.

c Number of cases identified as true by medical record review (i.e., sum of true positive and false negative cases).

d Number of cases meeting the criteria for each case-finding algorithm (i.e., sum of true positive and false positive cases).

e Number of cases meeting the criteria for each case-finding algorithm and identified as true by medical record review.

f Pseudosensitivity was calculated as the number of true positive cases for each case-finding algorithm divided by the sum of true positive and false negative cases. However, any true cases that did not meet the criteria for “possible cases” may have been excluded from the analysis.

AC: additional condition, BC: basic condition, CI: confidence interval, MDV: Medical Data Vision Co., Ltd., PPV: positive predictive values.