ABSTRACT

Lung cancer can be revealed by thromboembolic complications. Its association with pregnancy is becoming more frequent due to the increasing number of smoking women. The care of a pregnant woman with cancer is quite delicate because it requires finding a balance between the treatment of the mother and the potential foetal risk.

CASE PRESENTATION: The authors report the case of a 38-year-old patient, with a twin pregnancy of 16 weeks, complicated by proximal and distal peripheral venous thrombosis of the left lower limb under low molecular weight heparin therapy at a curative dose. A week later, the patient presented to the emergency room with respiratory distress associated with chest pain and low-abundance metrorrhagia. The obstetrical ultrasound performed confirmed the vitality of only one of the two foetuses. The transthoracic ultrasound objectified a very abundant pericardial effusion producing a tamponade, which was drained percutaneously and whose cytological study revealed a liquid rich in tumour cells. After the unfortunate death of the second twin and an endouterine evacuation, a chest computed tomography angiogram demonstrated a bilateral proximal pulmonary embolism associated with bilateral moderate pulmonary effusion as well as multiple thrombosis and secondary aspect liver lesions with a suspicious parenchymal lymph node of the upper lung lobe. A liver biopsy concluded to a secondary hepatic localization of a moderately differentiated adenocarcinoma whose immunohistochemical complement revealed a pulmonary origin. A multidisciplinary consultation meeting leaned towards treatment with neoadjuvant chemotherapy. The patient died 7 months later.

DISCUSSION: Venous thromboembolic disease is more common in pregnant women. Delayed diagnosis is common in these cases, resulting in a high rate of locally advanced or metastatic disease. Since the treatment of pregnancy-associated cancer does not rely on a standardized approach, the decision on how to proceed must be made by a multidisciplinary team.

CONCLUSION: The cornerstone of management remains to find the balance between treating the mother as well as possible while preventing the foetus from the possible harm of cytotoxic drugs frequently used to treat lung cancer. Because of the delayed diagnosis, the maternal prognosis often remains poor.

PMID:37228933 | PMC:PMC10205297 | DOI:10.1097/MS9.0000000000000516

15:18

PubMed articles on: Cancer & VTE/PE

Ten-Year Multicenter Retrospective Study Utilizing Machine Learning Algorithms to Identify Patients at High Risk of Venous Thromboembolism After Radical Gastrectomy

Int J Gen Med. 2023 May 18;16:1909-1925. doi: 10.2147/IJGM.S408770. eCollection 2023.

ABSTRACT

PURPOSE: This study aims to construct a machine learning model that can recognize preoperative, intraoperative, and postoperative high-risk indicators and predict the onset of venous thromboembolism (VTE) in patients.

PATIENTS AND METHODS: A total of 1239 patients diagnosed with gastric cancer were enrolled in this retrospective study, among whom 107 patients developed VTE after surgery. We collected 42 characteristic variables of gastric cancer patients from the database of Wuxi People's Hospital and Wuxi Second People's Hospital between 2010 and 2020, including patients' demographic characteristics, chronic medical history, laboratory test characteristics, surgical information, and patients' postoperative conditions. Four machine learning algorithms, namely, extreme gradient boosting (XGBoost), random forest (RF), support vector machine (SVM), and k-nearest neighbor (KNN), were employed to develop predictive models. We also utilized Shapley additive explanation (SHAP) for model interpretation and evaluated the models using k-fold cross-validation, receiver operating characteristic (ROC) curves, calibration curves, decision curve analysis (DCA), and external validation metrics.

RESULTS: The XGBoost algorithm demonstrated superior performance compared to the other three prediction models. The area under the curve (AUC) value for XGBoost was 0.989 in the training set and 0.912 in the validation set, indicating high prediction accuracy. Furthermore, the AUC value of the external validation set was 0.85, signifying good extrapolation of the XGBoost prediction model. The results of SHAP analysis revealed that several factors, including higher body mass index (BMI), history of adjuvant radiotherapy and chemotherapy, T-stage of the tumor, lymph node metastasis, central venous catheter use, high intraoperative bleeding, and long operative time, were significantly associated with postoperative VTE.

CONCLUSION: The machine learning algorithm XGBoost derived from this study enables the development of a predictive model for postoperative VTE in patients after radical gastrectomy, thereby assisting clinicians in making informed clinical decisions.

PMID:37228741 | PMC:PMC10202705 | DOI:10.2147/IJGM.S408770

15:18

PubMed articles on: Cancer & VTE/PE

Ambulatory cancer patients: who should definitely receive antithrombotic prophylaxis and who should never receive

Intern Emerg Med. 2023 May 25. doi: 10.1007/s11739-023-03306-8. Online ahead of print.

ABSTRACT

Up to 15-20% of cancer patients experience one or more episodes of venous thromboembolism during cancer disease. Approximately 80% of all cancer-associated venous thromboembolic events occur in non-hospitalized patients. Routine thromboprophylaxis for outpatients with cancer who start new anticancer treatment is currently not recommended by the international guidelines due to the high heterogeneity of these patients in terms of VTE or bleeding risks, the difficulties in selecting patients at high risk, and the uncertainty of duration of prophylaxis. Although the international guidelines endorsed the Khorana score for estimating the thrombotic risk in ambulatory cancer patients, the discriminatory performance of this score is not completely convincing and varies according to the cancer type. Consequently, a minority of ambulatory patients with cancer receive an accurate screening for primary prophylaxis of VTE. The aim of this review is to provide support to physicians in identifying those ambulatory patients with cancer for whom thromboprophylaxis should be prescribed and those that should not be candidate to thromboprophylaxis. In absence of high bleeding risk, primary thromboprophylaxis should be recommended in patients with pancreatic cancer and, probably, in patients with lung cancer harboring ALK/ROS1 translocations. Patients with upper gastrointestinal cancers are at high risk of VTE, but a careful assessment of bleeding risk should be made before deciding on antithrombotic prophylaxis. Primary prevention of VTE is not recommended in cancer patients at increased risk of bleeding as patients with brain cancer, with moderate-to-severe thrombocytopenia or severe renal impairment.

PMID:37227679 | DOI:10.1007/s11739-023-03306-8

15:18

PubMed articles on: Cancer & VTE/PE

Application of Machine Learning to the Prediction of Cancer-Associated Venous Thromboembolism

Res Sq. 2023 May 8:rs.3.rs-2870367. doi: 10.21203/rs.3.rs-2870367/v1. Preprint.

ABSTRACT

Venous thromboembolism (VTE) is a common and impactful complication of cancer. Several clinical prediction rules have been devised to estimate the risk of a thrombotic event in this patient population, however they are associated with limitations. We aimed to develop a predictive model of cancer-associated VTE using machine learning as a means to better integrate all available data, improve prediction accuracy and allow applicability regardless of timing for systemic therapy administration. A retrospective cohort was used to fit and validate the models, consisting of adult patients who had next generation sequencing performed on their solid tumor for the years 2014 to 2019. A deep learning survival model limited to demographic, cancer-specific, laboratory and pharmacological predictors was selected based on results from training data for 23,800 individuals and was evaluated on an internal validation set including 5,951 individuals, yielding a time-dependent concordance index of 0.72 (95% CI = 0.70-0.74) for the first 6 months of observation. Adapted models also performed well overall compared to the Khorana Score (KS) in two external cohorts of individuals starting systemic therapy; in an external validation set of 1,250 patients, the C-index was 0.71 (95% CI = 0.65-0.77) for the deep learning model vs 0.66 (95% CI = 0.59-0.72) for the KS and in a smaller external cohort of 358 patients the C-index was 0.59 (95% CI = 0.50-0.69) for the deep learning model vs 0.56 (95% CI = 0.48-0.64) for the KS. The proportions of patients accurately reclassified by the deep learning model were 25% and 26% respectively. In this large cohort of patients with a broad range of solid malignancies and at different phases of systemic therapy, the use of deep learning resulted in improved accuracy for VTE incidence predictions. Additional studies are needed to further assess the validity of this model.

PMID:37214902 | PMC:PMC10197737 | DOI:10.21203/rs.3.rs-2870367/v1

15:18

PubMed articles on: Cancer & VTE/PE

Effective long-term sirolimus treatment in hypoxemia mainly due to intrapulmonary right-to-left shunt in a patient with multiple vascular anomalies

Orphanet J Rare Dis. 2023 May 24;18(1):124. doi: 10.1186/s13023-023-02732-3.