Dataset Information

Hierarchical regression of ASA prediction model in predicting mortality prior to performing emergency laparotomy a systematic review

ABSTRACT:

Background

In light of increasing litigations around performing emergency surgery, various predictive tools are used for prediction of mortality prior to surgery. There are many predictive tools reported in literature, with ASA being one of the most widely accepted tools. Therefore, we attempted to perform a systematic review and meta-analysis to conclude ASA's ability in predicting mortality for emergency surgeries.

Methods

A wide literature search was conducted across MEDLINE and other databases using PubMed and Ovid with the following keywords; “Emergency laparotomy”, “Surgical outcomes”, “Mortality” and “Morbidity.” A total of 3989 articles were retrieved and only 11 articles met the inclusion criteria for this meta-analysis. Data was pooled and then analysed using the STATA 16.1 software. We conducted hierarchal regression between the following variables; mortality, gender, low ASA (ASA 1–2) and high ASA (ASA 3–5).

Results

1. High ASA was associated with a higher rate of mortality in males with ‘p’ value of 0.0001 at alpha value of 0.025. 2. The female gender itself showed a significantly high mortality rate, irrespective of low ASA or high ASA with ‘p’ value of 0.04 at alpha value of 0.05. 3. ITU admissions with a high ASA had a greater number of deaths compared to low ASA. ‘p’ value of 0.0054 at alpha value of 0.01.

Conclusion

Higher ASA showed a direct association with mortality and the male gender. The female gender was associated with a higher risk of mortality regardless of the ASA grades. Highlights • ASA is one of the most widely accepted predictive tools.• Higher ASA showed a direct association with mortality and the male gender.• The female gender was associated with higher risk of mortality regardless of the ASA.• ASA 4 and above are high-risk for emergency laparotomy. They should all be admitted to ITU for pre- and post-operative care.

SUBMITTER: Akhtar M

PROVIDER: S-EPMC7779956 | biostudies-literature | 2020 Dec

REPOSITORIES: biostudies-literature

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