Prediction of perioperative complications after robotic-assisted radical hysterectomy for cervical cancer using the modified surgical Apgar score

BACKGROUND: Although there has been marked development in surgical techniques, there is no easy and fast method of predicting complications in minimally invasive surgeries. We evaluated whether the modified surgical Apgar score (MSAS) could predict perioperative complications in patients undergoing robotic-assisted radical hysterectomy. METHODS: All patients with cervical cancer undergoing robotic-assisted radical hysterectomy at our institution between January 2011 and May 2017 were included. Their clinical characteristics were retrieved from their medical records. The surgical Apgar score (SAS) was calculated from the estimated blood loss, lowest mean arterial pressure, and lowest heart rate during surgery. We modified the SAS considering the lesser blood loss typical of robotic surgeries. Perioperative complications were defined using a previous study and the Clavien-Dindo classification and subdivided into intraoperative and postoperative complications. We analyzed the association of perioperative complications with low MSAS. RESULTS: A total of 138 patients were divided into 2 groups: with (n = 53) and without (n = 85) complications. According to the Clavien-Dindo classification, 49 perioperative complications were classified under Grade I (73.1%); 13, under Grade II (19.4%); and 5, under Grade III (7.5%); 0, under both Grade IV and Grade V. Perioperative complications were significantly associated with surgical time (p = 0.026). The MSAS had a correlation with perioperative complications (p = 0.047). The low MSAS (MSAS, ≤6; n = 52) group had significantly more complications [40 (76.9%), p = 0.01]. Intraoperative complications were more correlated with a low MSAS than were postoperative complications [1 (1.2%) vs. 21 (40.4%); p < 0.001, 13 (15.1%) vs. 25 (48.1%); p = 0.29, respectively]. We also analyzed the risk-stratified MSAS in 3 subgroups: low (MSAS, 7-10), moderate (MSAS 5-6), and high risks (MSAS, 0-4). The prevalence of intraoperative complications significantly increased as the MSAS decreased p = 0.01). CONCLUSIONS: This study was consistent the concept that the intuitive and simple MSAS might be more useful in predicting intraoperative complications than in predicting postoperative complications in minimally invasive surgeries, such as robotic-assisted radical hysterectomy for cervical cancer.ope

Developing Predictive Models for Risk of Postoperative Complications and Hemodynamic Instability in Patients Undergoing Surgery

Patients undergoing high-risk surgeries are often at higher risk of developing hemodynamic instability during surgery resulting in poor postoperative outcomes. This is usually associated with significantly increased postoperative morbidity and mortality, which therefore makes the early identification of these critical events and those patients at risk of postoperative complications crucial. With these motivations in mind, we first created a large deidentified research dataset of surgical case medical records from University of California, Irvine Medical Center (UCIMC) matched with physiological waveforms as well as intermittent vital sign values, lab values, and ventilator settings. To our knowledge, such a dataset does not currently exist for the intraoperative environment. We hope that creating a such a dataset will allow for advances in machine learning for intraoperative care. Using medical data from UCLA, we have developed deep neural network models to classify the risks of postoperative mortality, acute kidney injury, and reintubation utilizing readily available intraoperative information. Our risk scores were compared to currently commonly used risk indices ASA and Surgical Apgar as well as logistic regression. While the deep neural network models performed better than the risk scores and logistic regression, clinicians require additional information to assess what led to increased risk of complications. To address this, we also assessed the use of generalized additive neural networks (GANNs) to create a graphical look at how different features contributed to the risk of in hospital mortality. Finally, we were also interested in predicting critical intraoperative events to allow for time for the clinician to avoid such events. We focused on intraoperative hypotension as it is easier to define and has been shown to lead to increased risk of acute kidney injury, stroke, and myocardial injury. For the hypotension prediction models, we looked at the arterial pressure waveform and EMR data as inputs. Overall, these aims address a gap in current clinical decision guidance and support to reduce adverse events during surgery as well complications after

Measuring morbidity following major surgery

A systematic review of the efficacy of a specific perioperative haemodynamic management strategy was performed to explore the balance between therapeutic benefit and adverse effects. Whilst mortality and length of hospital stay were reduced in the intervention group, pooling of morbidity data for between-group comparisons was limited by the heterogeneity of morbidity reporting between different studies. Classification, criteria and summation of morbidity outcome variables were inconsistent between studies, precluding analyses of pooled data for many types of morbidity. A similar pattern was observed in a second systematic review of randomised controlled trials of perioperative interventions published in high impact surgical journals. The Post-operative Morbidity Survey (POMS), a previously published method of describing short-term postoperative morbidity, lacked validation. The POMS was prospectively collected in 439 patients undergoing elective major surgery in a UK teaching hospital. The prevalence and pattern of morbidity was described and compared with data from a similar study using the POMS in a US institution. The type and severity of surgery was reflected in the frequency and pattern of POMS defined postoperative morbidity. In the UK institution, many patients remained in hospital without morbidity as defined by the POMS, in contrast to the US institution, where very few patients remained in hospital in the absence of POMS defined morbidity. The POMS may have utility as a tool for recording bed occupancy and for modelling bed utilization. Inter-rater reliability was adequate and a priori hypotheses that the POMS would discriminate between patients with known measures of morbidity risk, and predict length of stay were generally supported through observation of data trends. The POMS was a valid descriptor of short-term post-operative morbidity in major surgical patients

Surgical outcomes in gynaecological oncology

Presently there are no reliable statistics available on complication rates associated with surgery in gynaecological cancer in the UK, apart from data from small studies involving individual centres and clinical trials. This thesis describes the United Kingdom Gynaecological Oncology Surgical Outcomes and Complications study (UKGOSOC) that was set up to prospectively capture data from ten UK gynaecological cancer centres on surgical procedures and complications in a uniform manner using agreed definitions so that data could be analysed and compared. A web-based database was set up to capture surgery and complications contemporaneously from the hospitals, and, consented women were sent a follow-up letter eight weeks postoperatively. Intraoperative and postoperative complications were recorded using a pre-determined list. Postoperative complications were graded (I-V) in increasing severity using the Clavien-Dindo system. Grade I complications were excluded from analysis. Univariable and multivariable regression analyses were performed to determine the predictors for intraoperative and postoperative complications. The Lasso method of penalised regression was used to create a risk-prediction model for comparing outcomes between the centres. Data on 2948 eligible major surgical procedures were analysed and 1462 follow-up letters were received. The overall intraoperative complication rate was 4.7% (95% CI 4.0-5.6). The hospital-reported postoperative complication rate was 14.4% (95% CI 13.2-15.7) which increased to 25.9% (95% CI 23.7-28.2) when both hospital and patient- reported postoperative complications were included. The predictors for intraoperative and postoperative complications were different apart from diabetes which was common to both. Risk-adjustment had a modest effect on the complication rates for individual centres but allowed for a fairer comparison. There was no concordance between the ranking order of the centres for intraoperative and postoperative complication rates. The overall intraoperative (≈5%) and postoperative (≈26%) complication rates and funnel graphs derived from this study could be used to benchmark performance of gynaecological oncology centres and even individual surgeons if a larger dataset becomes available nationally

Comparación de la calculadora ACS-NSQIP y el Apgar quirúrgico como predictores de complicaciones post colecistectomía

Determinar la efectividad de la calculadora ACS-NSQIP y el Apgar quirúrgico como herramientas de predicción de complicaciones post colecistectomía en el Hospital Regional Docente de Trujillo durante el período 2015 – 2019. Material y métodos: El presente estudio es transversal analítico realizado en pacientes mayores a 18 años con diagnóstico de colecistitis aguda sometidos a colecistectomía abierta o laparoscópica, atendidos en el Hospital Regional Docente de Trujillo durante el período 2015 - 2019. En el análisis bivariado se utilizó Chi cuadrado. Además, se utilizó el análisis de curva ROC para determinar la capacidad discriminativa de las calculadoras ACS-NSQIP y APGAR quirúrgico en la predicción de complicaciones graves de los pacientes. Resultados: Se estudiaron 227 pacientes de los cuales la media de edad de los pacientes que presentaron complicaciones graves fue 75,32 ± 14,58. Asimismo, 52.6% fueron pacientes del sexo masculino que presentaron complicaciones graves. Con respecto al análisis de predicción según la Curva ROC, la calculadora ACS-NSQIP mostró un área bajo la curva de 0,895 (IC 95% = 0,819 – 0,971; p = 0,01) y la calculadora Apgar quirúrgico mostró un área bajo la curva de 0,611 (IC 95% = 0,488 – 0,735; p = 0,11). Conclusiones: La calculadora ACS-NSQIP tiene efectividad en la predicción de complicaciones graves de los pacientes sometidos a colecistectomía por colecistitis aguda mientras que el Apgar quirúrgico no tiene efectividad en la predicción de dichas complicaciones.To determine the effectiveness of the ACS-NSQIP calculator and the surgical Apgar score as tools for predicting post-cholecystectomy complications at the Trujillo Regional Teaching Hospital during the period 2015-2019. Material and methods: The present study is an analytical cross-sectional study carried out in patients older than 18 years diagnosed with acute cholecystitis who underwent open or laparoscopic cholecystectomy, treated at the Trujillo Regional Teaching Hospital during the period 2015 - 2019. Chi-square was used in the bivariate analysis. In addition, ROC curve analysis was used to determine the discriminative ability of the ACS-NSQIP and surgical APGAR calculators in predicting serious complications in patients. Results: A total of 227 patients were studied, of whom the mean age of the patients who presented serious complications was 75.32 ± 14.58. Likewise, 52.6% were male patients who presented serious complications. Regarding the prediction analysis according to the ROC Curve, the ACS-NSQIP calculator showed an area under the curve of 0.895 (95%CI = 0,819 – 0,971; p = 0,01) and the surgical Apgar calculator showed an area under the curve of 0.611 (95%CI = 0,488 – 0,735; p = 0,11). Conclusions: The ACS-NSQIP calculator is effective in predicting serious complications in patients undergoing cholecystectomy for acute cholecystitis, while the surgical Apgar is ineffective in predicting such complications.Tesi