Validation of a post operative complication risk prediction algorithm in a non-head and neck squamous cell carcinoma cohort |
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| Institution: | 1. GSR Hospital, Hyderabad, Telangana, India;2. Department of Otorhinolaryngology, Head & Neck Surgery, Radboud University Medical Centre, Nijmegen, The Netherlands;3. Sri Sai College of Dental Surgery, Department of Oral and Maxillofacial Surgery, Vikarabad, Telangana, India;4. Department of Oral and Maxillofacial Surgery, Radboud University Medical Centre, Nijmegen, The Netherlands;5. Dept. Of Cranio-maxillofacial Surgery, AIIMS, Rishikesh, Uttarakhand, India;1. Head and Neck Academic Centre, University College London, Gower St, Bloomsbury, London WC1E 6BT, United Kingdom;2. Department of Oral and Maxillofacial Head and Neck Surgery, University College Hospital London, 235 Euston Rd, Bloomsbury, London NW1 2BU, United Kingdom;3. Department of Head and Neck Surgery, The University of Texas MD Anderson Cancer Center, Houston, TX, USA;1. Department of Oral & Maxillofacial surgery, All India Institute of Medical Sciences, New Delhi, India;2. Department of Physiology, All India Institute of Medical Sciences, New Delhi, India;1. Department of Oral & Maxillofacial Surgery, All India Institute of Medical Sciences, New Delhi, India;2. Department of Physiology, All India Institute of Medical Sciences, New Delhi, India;1. Leeds Teaching Hospitals Trust, Leeds General Infirmary, Great George Street, Leeds LS1 3EX, United Kingdom;2. University of Leeds, Worsley Building, University of Leeds, Woodhouse, Leeds LS2 9JT, United Kingdom;1. Department of Oromaxillofacial-Head and Neck Surgery, School of Stomatology, Oral Diseases Laboratory of Liaoning, China Medical University, Shenyang, China;2. Department of Oral and Maxillofacial Surgery, Faculty of Dentistry, Ibb University, Ibb, Yemen;3. Jibla University for Medical Sciences, Jibla Hospital, Ibb, Yemen;4. Department of Oral and Maxillofacial Surgery, Lanzhou University Second Hospital, Lanzhou, China |
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| Abstract: | Risk-adjusted algorithms in surgical audit attempt to adjust for patient case mix and complexity in order that published outcomes fairly reflect surgical performance and quality of care. Such risk-adjustment models have applied to head and neck squamous cell carcinoma (HNSCC). We test one algorithm, currently embedded in the oncology and reconstruction dataset within the Quality and Outcomes in Oral and Maxillofacial (QOMS) Audit, which is an artificial neural network, for its predictive accuracy on a surgical cohort receiving curative surgery for non-HNSCC pathology. A single centre retrospective case note audit of post operative complications between 2010 and 2020 was conducted on patients having curative surgery for non-HNSCC pathology. The observed complication rate was compared to the predicted probability of complications in order to test the performance of the complication risk-adjustment model. Of 1591 non-HNSCC patients, 58 met the inclusion criteria with a 30-day complication rate of 8/58 (13%). The artificial neural network predicted a complication rate of 20/58 (27%). Sensitivity (0.75), specificity (0.72) and overall accuracy (0.72) suggest acceptable discrimination. Hosmer-Lemershow Goodness of Fit test was good (p = 0.55) suggesting acceptable calibration though over-prediction of complication rate in the highest risk patents was observed. This external validation series suggests the algorithm can be applied to the non-HNSCC cohort, though some refinement of the algorithm is required to account for over-prediction of complications for higher-risk patients. With further analysis a robust means of risk adjusting for the non-HNSCC cohort should be possible. |
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| Keywords: | Audit Outcome Risk-adjustment Non-HNSCC Complication |
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