Additional file 1: of Evaluating the impact of prediction models: lessons learned, challenges, and recommendations

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<特集1>阪神・淡路大震災 第2部 体験談

<div><p>Background</p><p>The work ability index (WAI) is a frequently used tool in occupational health to identify workers at risk for a reduced work performance and for work-related disability. However, information about the prognostic value of the WAI to identify workers at risk for sickness absence is scarce.</p><p>Objectives</p><p>To investigate the prognostic value of the WAI for sickness absence, and whether the discriminative ability differs across demographic subgroups.</p><p>Methods</p><p>At baseline, the WAI (score 7-49) was assessed among 1,331 office workers from a Dutch financial service company. Sickness absence was registered during 12-months follow-up and categorised as 0 days, 0</p><p>Results</p><p>A lower WAI was associated with sickness absence (≥15 days vs. 0 days: per point lower WAI score OR=1.27; 95%CI 1.21-1.33). The WAI showed reasonable ability to discriminate between categories of sickness absence (ORC=0.65; 95%CI 0.63-0.68). Highest discrimination was found for comparing workers with ≥15 sick days with 0 sick days (AUC=0.77) or with 1-5 sick days (AUC=0.69). At the cut-off for poor work ability (WAI≤27) the sensitivity to identify workers at risk for ≥15 sick days was 7.5%, the specificity 99.6%, and the positive predictive value 82%. The performance was similar across demographic subgroups.</p><p>Conclusions</p><p>The WAI could be used to identify workers at high risk for prolonged sickness absence. However, due to low sensitivity many workers will be missed. Hence, additional factors are required to better identify workers at highest risk.</p></div

Pairwise AUCs: ability of the work ability index (WAI) to discriminate between categories of sickness absence.

<p>AUC: Area under the curve.</p

Univariate and multivariable multinomial regression analyses with odds ratios and 95% confidence intervals for the association between work ability, and individual factors with sickness absence among office workers (n = 1,331).

<p>*p-value <0.05; OR: odds ratio; 95% CI: 95% confidence interval; n: number of workers.</p><p>^a0 days of sickness absence is reference category.</p><p>^blower scores indicate better work ability.</p><p>Univariate and multivariable multinomial regression analyses with odds ratios and 95% confidence intervals for the association between work ability, and individual factors with sickness absence among office workers (n = 1,331).</p

Discriminative ability of the WAI dimensions in the prediction of different durations of sickness absence among office workers (n = 1,331).

<p>*p-value <0.05; ORC: ordinal c-index; 95% CI: 95% confidence interval; Dim: dimension.</p><p>^adiscriminative ability of the single WAI dimensions.</p><p>^bdiscriminative ability of the total WAI score minus a dimension.</p><p>Discriminative ability of the WAI dimensions in the prediction of different durations of sickness absence among office workers (n = 1,331).</p

Scoring system CaFaSpA referral rule: applicable in primary care patients with chronic low back pain.

<p>Scoring system CaFaSpA referral rule: applicable in primary care patients with chronic low back pain.</p

Performance of the referral rule in the validation data (CaFaSpA 2).

<p>Performance of the referral rule in the validation data (CaFaSpA 2).</p

Results of the multivariable logistic regression analyses in the validation data (CaFaSpA 2), development data (CaFaSpA 1) and the two data sets combined; odds ratio’s (95% confidence interval).

<p>LBP = low back pain; NSAIDs = nonsteriodal anti-inflammatory drugs; SpA = spondyloarthritis.</p><p>^† A positive ASAS questionnaire is achieved when at least 4 out of 5 questions are answered positively.</p><p>Results of the multivariable logistic regression analyses in the validation data (CaFaSpA 2), development data (CaFaSpA 1) and the two data sets combined; odds ratio’s (95% confidence interval).</p

Combined model different cut points for referral rule with corresponding sensitivity and specificity.

<p>Combined model different cut points for referral rule with corresponding sensitivity and specificity.</p

Patient and process outcome.

<p>SBI = serious bacterial infection.</p><p>UTI = urinary tract infection.</p><p>^a Median (25–75 percentiles).</p><p>^# including hemoglobin, leukocyte, thrombocyte and differential count.</p><p>^~including feces culture, nasal swab, throat culture and cerebrospinal fluid (CSF) culture.</p><p>*Chi-square, p-value <0.05.</p><p>^± Overall diagnostics minus urine-dipstick analysis.</p><p>^$1 In 17 of 19 children with pneumonia chest-radiography was performed.</p><p>^$2 In 12 of 14 children with pneumonia chest-radiography was performed.</p><p>^$3 In 175 of 200 children without pneumonia no chest-radiography was performed.</p><p>^$4 In 190 of 206 children without pneumonia no chest-radiography was performed.</p><p>^$5 In 6 of 6 children with UTI a urine-culture was performed.</p><p>^$6 In 8 of 9 children with UTI a urine-culture was performed.</p><p>^$7 In 201 of 213 children without UTI no urine-culture was performed.</p><p>^$8 In 202 of 211 children without UTI no urine-culture was performed.</p><p>Patient and process outcome.</p