A comparative study of four thoracic mortality scores

BACKGROUND

The percentage of patients in resectable stages at initial diagnosis of non-small cell lung cancer (NSCLC) raises due to better screening programs. Therefore, risk prediction models are becoming more critical. Here, we validated and compared four established scoring models, the Thoracoscore, Epithor, Eurloung 2, and the simplified Eurolung 2 (2b), in their ability to predict 30-day mortality.

METHODS

All consecutive patients undergoing anatomical pulmonary resection were included. The performance of the four scoring systems was assessed with Hosmer-Lemeshow goodness-of-fit test (calibration) and receiver operating characteristic (ROC) curves (discrimination). We compared the area under the curve (AUC) of the ROC curves by DeLong's method.

RESULTS

A total of 624 patients underwent surgery for NSCLC at our institution between 2012 and 2018 30-day mortality was 2.2% (14 patients). The AUC for Eurolung 2 and the simplified Eurolung 2 (0.82) were greater than those of the other scoring systems, Epithor (0.71) and Thoracoscore (0.65). In addition, the DeLong analysis showed a significant superiority of Eurolung 2 and Eurolung 2b over the Thoracoscore (p = 0.04); there were no significant differences compared to Epithor.

CONCLUSION

Eurolung 2 and the simplified Eurolung 2 were the favorable scoring systems for predicting 30-day mortality compared to Thoracoscore and Epithor. Therefore, we recommend using Eurolung 2 or the simplified Eurolung 2 for preoperative risk stratification.

Thoracoscore: Does it predict mortality in the Indian scenario? – A retrospective study

Background and Aims:

Preoperative risk stratification helps in better prognostication and allocation of resources. However, risk scoring models are less often used in thoracic surgery. Thoracoscore, a risk score model for thoracic surgery was originally developed on a French population and was later validated in many countries. As there is no literature on its ability to predict mortality in the Indian population, we aimed to validate Thoracoscore in Indian thoracic surgical patients.

Methods:

This retrospective study was carried out in a tertiary care centre after obtaining institutional ethics committee clearance. Patients who were operated for lung pathologies via a posterolateral thoracotomy incision between January 2014 and December 2018 were included in the study. Data on Thoracoscore variables and few additional factors (pulmonary arterial hypertension (PAH), redo surgery, blood loss, blood transfusion, duration of anaesthesia, one lung ventilation and surgery) was collected along with observed mortality statistics. Mortality was predicted using online calculator from the site https://sfar.org/scores2/thoracoscore2.php.Significant continuous and categorical variables in causation of mortality were identified using unpaired t-test and Chi-square tests, respectively. These variables were subjected to multivariate logistic regression to find independent risk factors for mortality. The calibration and discrimination of the Thoracoscore model was analysed by using Hosmer–Lemeshow test and area under the curve of receiver operating characteristic curves.

Results:

Overall observed mortality in the study was 3.2% while predicted mortality was 0.44%. The Thoracoscore had poor calibration and fair discrimination ability. PAH and re-operative surgery along with Thoracoscore were found to be independent risk factors of mortality in thoracic surgery.

Conclusion:

Thoracoscore fails to predict mortality in the Indian population.

Pneumonectomy for primary lung cancer: contemporary outcomes, risk factors and model validation

OBJECTIVES

Despite the increased rate of adverse outcomes compared to lobectomy, for selected patients with lung cancer, pneumonectomy is considered the optimal treatment option. The objective of this study was to identify risk factors for mortality in patients undergoing pneumonectomy for primary lung cancer.

METHODS

Data from all patients undergoing pneumonectomy for primary lung cancer at 2 large thoracic surgical centres between 2012 and 2018 were analysed. Multivariable logistic and Cox regression analyses were used to identify risk factors associated with 90-day and 1-year mortality and reduced long-term survival, respectively.

RESULTS

The study included 256 patients. The mean age was 65.2 (standard deviation 9.4) years. In-hospital, 90-day and 1-year mortality were 6.3% (n = 16), 9.8% (n = 25) and 28.1% (n = 72), respectively. The median follow-up time was 31.5 months (interquartile range 9-58 months). Patients who underwent neoadjuvant therapy had a significantly increased risk of 90-day [odds ratio 6.451, 95% confidence interval (CI) 1.867-22.291, P = 0.003] and 1-year mortality (odds ratio 2.454, 95% CI 1.079-7.185, P = 0.044). Higher Performance Status score was associated with higher 1-year mortality (odds ratio 2.055, 95% CI 1.248-3.386, P = 0.005) and reduced overall survival (hazard ratio 1.449, 95% CI 1.086-1.934, P = 0.012). Advanced (stage III/IV) disease was associated with reduced overall survival (hazard ratio 1.433, 95% CI 1.019-2.016, P = 0.039). Validation of a pneumonectomy-specific risk model demonstrated inadequate model performance (area under the curve 0.54).

CONCLUSIONS

Pneumonectomy remains associated with a high rate of perioperative mortality. Neoadjuvant chemoradiotherapy, Performance Status score and advanced disease emerged as the key variables associated with adverse outcomes after pneumonectomy in our cohort.

A systematic review of risk prediction models for perioperative mortality after thoracic surgery

OBJECTIVES

Guidelines advocate that patients being considered for thoracic surgery should undergo a comprehensive preoperative risk assessment. Multiple risk prediction models to estimate the risk of mortality after thoracic surgery have been developed, but their quality and performance has not been reviewed in a systematic way. The objective was to systematically review these models and critically appraise their performance.

METHODS

The Cochrane Library and the MEDLINE database were searched for articles published between 1990 and 2019. Studies that developed or validated a model predicting perioperative mortality after thoracic surgery were included. Data were extracted based on the checklist for critical appraisal and data extraction for systematic reviews of prediction modelling studies.

RESULTS

A total of 31 studies describing 22 different risk prediction models were identified. There were 20 models developed specifically for thoracic surgery with two developed in other surgical specialties. A total of 57 different predictors were included across the identified models. Age, sex and pneumonectomy were the most frequently included predictors in 19, 13 and 11 models, respectively. Model performance based on either discrimination or calibration was inadequate for all externally validated models. The most recent data included in validation studies were from 2018. Risk of bias (assessed using Prediction model Risk Of Bias ASsessment Tool) was high for all except two models.

CONCLUSIONS

Despite multiple risk prediction models being developed to predict perioperative mortality after thoracic surgery, none could be described as appropriate for contemporary thoracic surgery. Contemporary validation of available models or new model development is required to ensure that appropriate estimates of operative risk are available for contemporary thoracic surgical practice.

[Thoracic surgery and co-morbid patients]

Patients undergoing lung resection are often active or former smokers who have chronic disease related to tobacco (coronary artery disease, chronic obstructive bronchitis, arteritis of the inferior limbs…). Those co-morbidities increase the operative risk for surgery in which morbi-mortality is relevant. On top of this, we are witnessing an increasing number of non-small cell lung cancers in an aging population that might show signs of frailty. The challenge for the surgeon is to identify early those co-morbid and/or frail patients by using predictive scores like Thoracoscore, mFI (modified frailty index) or MSK-F1 (Memorial Sloan-Kettering Frailty Index). Screening for those high-risk patients implies adapting surgical management through a multidisciplinary approach. That is the objective of co-managment, which allows geriatricians and surgeons to collaborate, or enhanced recovery after surgery which is more accessible to all group ages. The objective of this article is to review the management of co-morbid patients in thoracic surgery, by detailing certain predictive scores available and the multidisciplinary approaches developed to treat the patients screened.

Dynamic nomogram for long-term survival in patients with non-small cell lung cancer after pneumonectomy

Background

The study aims to identify prognostic factors of overall survival (OS) in patients who had pneumonectomy, in order to develop a practical dynamic nomogram model.

Methods

A total of 2,255 patients with non-small cell lung cancer (NSCLC) who underwent pneumonectomy were identified from 2010-2015 in the Surveillance, Epidemiology, and End Results (SEER) database. The cohort was divided into a training (2011-2015) and a validation [2010] cohort. A nomogram and a risk classification system were constructed from the independent survival factors in multivariable analysis. The predictive accuracy of the nomogram was measured through internal and external validation.

Results

Independent prognostic factors associated with OS were gender, age, pathology, tumor size, N stage, chemotherapy, and radiotherapy. The C-index of the nomogram for OS was 0.675 (95% CI: 0.655-0.694). Similarly, the AUC of the model was 0.733, 0.709, and 0.701 for the 1-, 3-, and 5-year OS, respectively. The calibration curves for survival demonstrated good agreement. Significant statistical differences were found in the OS of patients within different risk groups. An online calculation tool was established for clinical use.

Conclusions

This novel nomogram was able to provide a reliable prognosis for survival in patients with NSCLC undergoing pneumonectomy.

External validation of six existing multivariable clinical prediction models for short-term mortality in patients undergoing lung resection

 

OBJECTIVES

National guidelines advocate the use of clinical prediction models to estimate perioperative mortality for patients undergoing lung resection. Several models have been developed that may potentially be useful but contemporary external validation studies are lacking. The aim of this study was to validate existing models in a multicentre patient cohort.

METHODS

The Thoracoscore, Modified Thoracoscore, Eurolung, Modified Eurolung, European Society Objective Score and Brunelli models were validated using a database of 6600 patients who underwent lung resection between 2012 and 2018. Models were validated for in-hospital or 30-day mortality (depending on intended outcome of each model) and also for 90-day mortality. Model calibration (calibration intercept, calibration slope, observed to expected ratio and calibration plots) and discrimination (area under receiver operating characteristic curve) were assessed as measures of model performance.

RESULTS

Mean age was 66.8 years (±10.9 years) and 49.7% (n = 3281) of patients were male. In-hospital, 30-day, perioperative (in-hospital or 30-day) and 90-day mortality were 1.5% (n = 99), 1.4% (n = 93), 1.8% (n = 121) and 3.1% (n = 204), respectively. Model area under the receiver operating characteristic curves ranged from 0.67 to 0.73. Calibration was inadequate in five models and mortality was significantly overestimated in five models. No model was able to adequately predict 90-day mortality.

CONCLUSIONS

Five of the validated models were poorly calibrated and had inadequate discriminatory ability. The modified Eurolung model demonstrated adequate statistical performance but lacked clinical validity. Development of accurate models that can be used to estimate the contemporary risk of lung resection is required.

[Approaches to the pre-operative functional assessment of patients with lung cancer and preoperative rehabilitation]

Surgery is the best treatment for early lung cancer but requires a preoperative functional evaluation to identify patients who may be at a high risk of complications or death. Guideline algorithms include a cardiological evaluation, a cardiopulmonary assessment to calculate the predicted residual lung function, and identify patients needing exercise testing to complete the evaluation. According to most expert opinion, exercise tests have a very high predictive value of complications. However, since the publication of these guidelines, minimally-invasive surgery, sublobar resections, prehabilitation and enhanced recovery after surgery (ERAS) programmes have been developed. Implementation of these techniques and programs is associated with a decrease in postoperative mortality and complications. In addition, the current guidelines and the cut-off values they identified are based on early series of patients, and are designed to select patients before major lung resection (lobectomy-pneumonectomy) performed by thoracotomy. Therefore, after a review of the current guidelines and a brief update on prehabilitation (smoking cessation, exercise training and nutritional aspects), we will discuss the need to redefine functional criteria to select patients who will benefit from lung surgery.

"Fit for Surgery? What's New in Preoperative Assessment of the High-Risk Patient Undergoing Pulmonary Resection"

Advances in perioperative assessment and diagnostics, together with developments in anesthetic and surgical techniques, have considerably expanded the pool of patients who may be suitable for pulmonary resection. Thoracic surgical patients frequently are perceived to be at high perioperative risk due to advanced age, level of comorbidity, and the risks associated with pulmonary resection, which predispose them to a significantly increased risk of perioperative complications, increased healthcare resource use, and costs. The definition of what is considered "fit for surgery" in thoracic surgery continually is being challenged. However, no internationally standardized definition of prohibitive risk exists. Perioperative assessment traditionally concentrates on the "three-legged stool" of pulmonary mechanical function, parenchymal function, and cardiopulmonary reserve. However, no single criterion should exclude a patient from surgery, and there are other perioperative factors in addition to the tripartite assessment that need to be considered in order to more accurately assess functional capacity and predict individual perioperative risk. In this review, the authors aim to address some of the more erudite concepts that are important in preoperative risk assessment of the patient at potentially prohibitive risk undergoing pulmonary resection for malignancy.

Performance Comparison Between SURPAS and ACS NSQIP Surgical Risk Calculator in Pulmonary Resection

BACKGROUND

Accurate preoperative risk assessment is critical for informed decision making. The Surgical Risk Preoperative Assessment System (SURPAS) and the National Surgical Quality Improvement Program (NSQIP) Surgical Risk Calculator (SRC) predict risks of common postoperative complications. This study compares observed and predicted outcomes after pulmonary resection between SURPAS and NSQIP SRC.

METHODS

Between January 2016 and December 2018, 2514 patients underwent pulmonary resection and were included. We entered the requisite patient demographics, preoperative risk factors, and procedural details into the online NSQIP SRC and SURPAS formulas. Performance of the prediction models was assessed by discrimination and calibration.

RESULTS

No statistically significant differences were found between the 2 models in discrimination performance for 30-day mortality, urinary tract infection, readmission, and discharge to a nursing or rehabilitation facility. The ability to discriminate between a patient who will develop a complication and a patient who will not was statistically indistinguishable between NSQIP and SURPAS, except for renal failure. With a C index closer to 1.0, the NSQIP performed significantly better than the SURPAS SRC in discriminating risk of renal failure (C index, 0.798 vs 0.694; P = .003). The calibration curves of predicted and observed risk for each model demonstrate similar performance with a tendency toward overestimation of risk, apart from renal failure.

CONCLUSIONS

Overall, SURPAS and NSQIP SRC performed similarly in predicting outcomes for pulmonary resections in this large, single-center validation study with moderate to good discrimination of outcomes. Notably, SURPAS uses a smaller set of input variables to generate the preoperative risk assessment. The addition of thoracic-specific input variables may improve performance.

Assessment and Optimisation of Lung Cancer Patients for Treatment with Curative Intent

Over the past decade the field of lung cancer management has seen many developments. Coupled with an ageing population and increasing rates of comorbid illness, the work-up for treatments with curative intent has become more complex and detailed. As well as improvements in imaging and staging techniques, developments in both surgery and radiotherapy may now allow patients who would previously have been considered unfit or not appropriate for treatment with curative intent to undergo radical therapies. This overview will highlight published studies relating to investigation and staging techniques, together with assessments of fitness, with the aim of helping clinicians to determine the most appropriate treatments for each patient. We also highlight areas where further research may be required.

Development and validation of a risk score for predicting death after pneumonectomy

Pneumonectomy is associated with significant postoperative mortality. This study was undertaken to develop and validate a risk model of mortality following pneumonectomy. We reviewed our prospective database and identified 774 pneumonectomies from a total of 7792 consecutive anatomical lung resections in the years 2003 to 2010 (rate of pneumonectomy: 9.9%). Based on data from 542 pneumonectomies between 2003 and 2007 (i.e., the "discovery set"), a penalized multivariable logistic regression analysis was performed to identify preoperative risk factors. A risk model was developed and validated in an independent data set of 232 pneumonectomies that were performed between 2008 and 2010 (i.e., the "validation set"). Of the 542 patients in the discovery set (DS), 35 patients (6.5%) died after pneumonectomy during the same admission. We developed a risk prediction model for in-hospital mortality following pneumonectomy; that model included age, current alcohol use, coronary artery disease, preoperative leukocyte count and palliative indication as possible risk factors. The risk model was subsequently successfully validated in an independent data set (n = 232) in which 18 patients (7.8%) died following pneumonectomy. For the validation set, the sensitivity of the model was 53.3% (DS: 54.3%), the specificity was 88.0% (DS: 87.4%), the positive predictive value was 26.7% (DS: 22.9%) and the negative predictive value was 95.8% (DS: 96.5%). The Brier score was 0.062 (DS: 0.054). The prediction model is statistically valid and clinically relevant.

Thoracoscore and European Society Objective Score Fail to Predict Mortality in the UK

Background

Thoracoscore and the European Society Objective Score (ESOS.01) are two scoring systems used in thoracic surgery to estimate operative mortality risk. We aimed to evaluate if these are valid tools for use in the UK population.

Methods

A multi-center, prospective study was carried out on patients undergoing lung resection at six UK centers. Data were submitted electronically using our online data collection tool. Data were analyzed to determine the factors affecting mortality. A receiver operating characteristic analysis determined the ability of the thoracoscore and ESOS.01 to predict in-hospital mortality.

Results

Data were complete for 2,245 patients. The observed in-hospital mortality was 31 patients (1.38%). Mean thoracoscore was 2.66 (SD ± 3.21). Gender (P = 0.004, hazard ratio 4.786) and co-morbidity score (P = 0.005, hazard ratio 3.289) were identified as risk factors for mortality. A sub-analysis was performed using data from 1,912 patients with complete data for ESOS.01. In this group, mean thoracoscore was 2.55 (SD ± 2.94), mean ESOS.01 was 2.11(SD ± 1.41), and these were statistically significantly different (P < 0.0001). The observed in-hospital mortality was 28 patients (1.46%). The c-index for thoracoscore was 0.705, and for ESOS.01 was 0.739.

Conclusions

Both thoracoscore and ESOS.01 overestimated mortality in the UK population. There is a continued need to develop an appropriate risk prediction system for the UK.