LAUSR

BACKGROUND Nursing diagnoses should reasonably represent global nursing practice phenomena, organizing indicators in their clinical structure that represent different scenarios and populations. However, few studies have summarized the evidence of these indicators, mainly for behavioral diagnoses. AIM This systematic review aimed to identify the best clinical indicators (CI) to determine the presence or absence of the nursing diagnosis "Ineffective Health Management" (IHM). METHOD A systematic review with meta-analysis was utilized. Six electronic databases were consulted to retrieve studies that identified the nursing diagnosis IHM, with at least one CI. The period of data collection was between September and October 2020. The research group independently conducted the selection, quality assessment, data extraction, and analysis of all included studies. Fixed-effect measures and meta-analyses summarized sensitivity, specificity measures, and diagnostic odds ratios using the statistical software R. The preferred reporting items for systematic reviews and meta-analyses and standards for reporting studies of diagnostic accuracy guidelines were used to guide this review, and quality assessment of diagnostic accuracy studies was used for the critical appraisal of the methodological quality of the included studies. RESULTS The systematic review included 11 studies on people with chronic conditions, the elderly, and pregnant women. The analyzed four CI showed diagnostic odds ratios statistically higher than the unit value, highlighting the "Failure to include the treatment regimen in daily living" (DOR = 45.53; CI = 10.1, 205.6). LINKING EVIDENCE TO ACTION Overall, findings showed that all CI of the IHM nursing diagnosis had good sensitivity, specificity, and diagnostic odds ratio measures to identify their presence correctly. These findings can contribute to better accuracy in nurses' decision-making process, providing indicators to infer the IHM nursing diagnosis early in different population spectra based on the best measures of diagnostic accuracy.