Literature DB >> 16914285

Risk adjusted surgical audit in gynaecological oncology: P-POSSUM does not predict outcome.

N Das1, A S Talaat, R Naik, A D Lopes, K A Godfrey, M H Hatem, R J Edmondson.   

Abstract

AIMS: To assess the Physiological and Operative Severity Score for the enumeration of mortality and morbidity (POSSUM) and its validity for use in gynaecological oncology surgery.
METHODS: All patients undergoing gynaecological oncology surgery at the Northern Gynaecological Oncology Centre (NGOC) Gateshead, UK over a period of 12months (2002-2003) were assessed prospectively. Mortality and morbidity predictions using the Portsmouth modification of the POSSUM algorithm (P-POSSUM) were compared to the actual outcomes. Performance of the model was also evaluated using the Hosmer and Lemeshow Chi square statistic (testing the goodness of fit).
RESULTS: During this period 468 patients were assessed. The P-POSSUM appeared to over predict mortality rates for our patients. It predicted a 7% mortality rate for our patients compared to an observed rate of 2% (35 predicted deaths in comparison to 10 observed deaths), a difference that was statistically significant (H&amp;L chi(2)=542.9, d.f. 8, p<0.05).
CONCLUSION: The P-POSSUM algorithm overestimates the risk of mortality for gynaecological oncology patients undergoing surgery. The P-POSSUM algorithm will require further adjustments prior to adoption for gynaecological cancer surgery as a risk adjusted surgical audit tool.

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Year:  2006        PMID: 16914285     DOI: 10.1016/j.ejso.2006.06.010

Source DB:  PubMed          Journal:  Eur J Surg Oncol        ISSN: 0748-7983            Impact factor:   4.424


  5 in total

1.  Perioperative mortality and morbidity prediction using POSSUM, P-POSSUM and APACHE II in Chinese gastric cancer patients: surgical method is a key independent factor affecting prognosis.

Authors:  Yantian Fang; Chunhsien Wu; Xiaodong Gu; Zhengyang Li; Jianbin Xiang; Zongyou Chen
Journal:  Int J Clin Oncol       Date:  2013-03-09       Impact factor: 3.402

2.  Prediction model for 30-day morbidity after gynecological malignancy surgery.

Authors:  Seung-Hyuk Shim; Sun Joo Lee; Meari Dong; Jung Hwa Suh; Seo Yeon Kim; Ji Hye Lee; Soo-Nyung Kim; Soon-Beom Kang; Jayoun Kim
Journal:  PLoS One       Date:  2017-06-01       Impact factor: 3.240

3.  Factors Predicting Surgical Effort Using Explainable Artificial Intelligence in Advanced Stage Epithelial Ovarian Cancer.

Authors:  Alexandros Laios; Evangelos Kalampokis; Racheal Johnson; Sarika Munot; Amudha Thangavelu; Richard Hutson; Tim Broadhead; Georgios Theophilou; Chris Leach; David Nugent; Diederick De Jong
Journal:  Cancers (Basel)       Date:  2022-07-15       Impact factor: 6.575

4.  Predictors of complications in gynaecological oncological surgery: a prospective multicentre study (UKGOSOC-UK gynaecological oncology surgical outcomes and complications).

Authors:  R Iyer; A Gentry-Maharaj; A Nordin; M Burnell; R Liston; R Manchanda; N Das; R Desai; R Gornall; A Beardmore-Gray; J Nevin; K Hillaby; S Leeson; A Linder; A Lopes; D Meechan; T Mould; S Varkey; A Olaitan; B Rufford; A Ryan; S Shanbhag; A Thackeray; N Wood; K Reynolds; U Menon
Journal:  Br J Cancer       Date:  2014-12-23       Impact factor: 7.640

5.  Application of gene expression programming and neural networks to predict adverse events of radical hysterectomy in cervical cancer patients.

Authors:  Maciej Kusy; Bogdan Obrzut; Jacek Kluska
Journal:  Med Biol Eng Comput       Date:  2013-10-18       Impact factor: 2.602

  5 in total

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