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TGA Surgical Outcome Score

: Explanation and Clinical Context This tool estimates the probability of an adverse surgical outcome (e.g., mortality or major complication) after repair of transposition of the great arteries, based on key preoperative and intraoperative variables (age at surgery, ventricular function, presence of a VSD, complexity of coronary anatomy, ischemic time). In this sample implementation, a logistic regression model is as

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TGA Surgical Outcome Score: Explanation and Clinical Context This tool estimates the probability of an adverse surgical outcome (e.g., mortality or major complication) after repair of transposition of the great arteries, based on key preoperative and intraoperative variables (age at surgery, ventricular function, presence of a VSD, complexity of coronary anatomy, ischemic time). In this sample implementation, a logistic regression model is assumed, with arbitrary coefficients for demonstration purposes. You should replace them with values derived from your validated source.

The estimated probability p is calculated via the logistic function: logit(p) = a + b1·(age) + b2·(ventricular_function) + b3·(VSD) + b4·(coronary_risk) + b5·(ischemia_time)p = e^(logit(p)) / (1 + e^(logit(p))) You should validate and calibrate this model in a suitable cohort before clinical use. Reference / Suggested Sources to Adapt Model: – Morfaw et al., “Outcomes after corrective surgery for congenital dextro-TGA” (PMCID) – Williams et al., “Surgical outcomes in congenital heart disease” – Consider also studies of arterial switch complications and coronary risk in TGA repairs.

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This active worksheet captures and exports structured inputs locally. It does not assert a numerical formula or evidence claim without a reproducible primary source.