Proposal of a Multivariable Prediction Model for Graded Morbidity after Liver Resection for Colorectal Metastases

Abstract

Background Prognostic models to predict individual early postoperative morbidity after liver resection for colorectal liver metastases (CLM) are not available but could enable optimized preoperative patient selection and postoperative surveillance for patients at greater risk of complications. The aim of this study was to establish a prognostic model for the prediction of morbidity after liver resection graded according to Dindo.

Methods N = 679 cases of primary liver resection for CLM were retrospectively analyzed using univariable and multivariable ordinal regression analyses. Receiver operating characteristics curve (ROC) analysis was utilised to assess the sensitivity and specificity of predictions and their potential usefulness as prognostic models. Internal validation of the score was performed using data derived from 129 patients.

Results The final multivariable regression model revealed lower preoperative levels, a greater number of units of intraoperatively transfused packed red blood cells (pRBCs), longer duration of surgery, and larger metastases to independently influence postoperatively graded morbidity. ROC curve analysis demonstrated that the multivariable regression model is able to predict each individual grade of postoperative morbidity with high sensitivity and specificity. The areas under the receiver operating curves (AUROC) for all of these predictions of individual grades of morbidity were > 0.700, indicating potential usefulness as a predictive model. Moreover, a consistent concordance in Grades I, II, IV, and V according to the classification proposed by Dindo et al. was observed in the internal validation.

Conclusion This study proposes a prognostic model for the prediction of each grade of postoperative morbidity after liver resection for CLM with high sensitivity and specificity using pre- and intraoperatively available variables.

Zusammenfassung

Hintergrund Prognostische Modelle zur Vorhersage der individuellen früh-postoperativen Morbidität nach Leberresektion von kolorektalen Lebermetastasen sind nicht verfügbar, könnten aber eine optimierte präoperative Patientenselektion und postoperative Überwachung von Patienten mit erhöhtem Risiko ermöglichen. Ziel dieser Studie war daher ein prognostisches Modell für die Vorhersage der Morbidität nach Leberresektion klassifiziert nach Dindo zu etablieren.

Methoden N = 679 Fälle von primären Leberresektionen kolorektaler Lebermetastasen wurden retrospektiv unter Nutzung von multivariabel und ordinalen Regressionsanalysen ausgewertet. Analysen der Receiver operating characteristics curve (ROC) wurden zur Überprüfung der Sensitivität und Spezifität der Vorhersagen und der potenziellen Bedeutung als prognostisches Modell eingesetzt. Das Modell wurde anhand weiterer 129 Fälle intern validiert.

Ergebnisse Das finale multivariable Regressionsmodel zeigte für niedrige präoperative Hb-Werte, hohen intraoperativen Transfusionsbedarf, lange OP-Zeiten sowie größere Metastasen einen deutlichen Einfluss auf die postoperative Morbidität. Die Analyse der ROC-curve beweist die Fähigkeit des multivariable Regressionsmodels die einzelnen Grade postoperativer Morbidität mit hoher Spezifität und Sensitivität vorherzusagen. Die “Area under the receiver operating curve” (AUROC) für die Vorhersage aller einzelnen Morbiditätsgrade von > 0,700 unterstreicht den potenziellen Nutzen als prognostisches Model. Die interne Validierung zeigt eine klare Konkordanz hinsichtlich der Grade I, II, IV und V nach Dindo.

Schlussfolgerung Diese Studie schlägt ein prognostisches Modell für die Vorhersage der postoperativen Morbidität nach Leberresektion von kolorektalen Lebermetastasen mit hoher Sensitivität und Spezifität unter Nutzung von prä- und intraoperativen Variablen vor.

Key words

Clavin-Dindo classification - prognostic models - risk assessment - prognosis - multivariable ordinal regression models

Schlüsselwörter

Clavin-Dindo Klassifikation - prognostische Modelle - Risikoabschätzung - multivariable ordinale Regressionsmodelle - Prognose

Publikationsverlauf

Artikel online veröffentlicht:
22. Oktober 2020

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