Dataset Information

Prognosis assessment model based on low serum calcium in patients with acute pulmonary thromboembolism.

ABSTRACT:

Background and objective

The pulmonary embolism severity index (PESI) and simplified PESI (sPESI) are recommended to recognize patients with acute pulmonary thromboembolism (PTE) with low prognosis risk, which is of great significance for treatment. This study aims to verify the influence of hypocalcaemia on the prognosis of patients with PTE and to establish a new prognosis assessment model.

Methods

This is an observational, multicentre study enrolling patients with PTE from February 2010 to June 2020 across 12 Chinese hospitals. Variables in PESI, serum calcium levels and patient survival status as of 5 July 2020 were collected. The area under the curve of the receiver operating characteristic curve, sensitivity, specificity and Youden index were used to evaluate model performance.

Results

In the cohort of 4196 patients with PTE, independent associations existed between hypocalcaemia and mid- and long-term mortalities (p <0.05). By including hypocalcaemia, the new 30-day death risk prediction rule, Peking Union Medical College Hospital rule (PUMCH rule), showed significantly higher specificity (0.622 [0.582, 0.661]; p <0.001) than the PESI (0.514 [0.473, 0.554]) and sPESI (0.484 [0.444, 0.525]) and similar sensitivity (0.963 [0.810, 0.999]; p = 0.161) with PESI (0.889 [0.708, 0.976]) and sPESI (0.963 [0.810, 0.999]) in the internal validation cohort. Well-performing predictive validity was also verified on a constructed external validation cohort.

Conclusion

Hypocalcaemia is independently associated with mid- and long-term PTE mortalities. The PUMCH rule showed significantly higher specificity than the PESI and sPESI and similar sensitivity, which may be used as a prognostic assessment tool for patients with acute PTE.

SUBMITTER: Yang YQ

PROVIDER: S-EPMC9540334 | biostudies-literature | 2022 Aug

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Prognosis assessment model based on low serum calcium in patients with acute pulmonary thromboembolism.

Yang Yu-Qing YQ Wang Xin X Zhang Yun-Jian YJ Chen Yan-Fan YF Wang Ling L Hu Xiao-Wen XW Niu Ling L Pu Hong-Mei HM Zhang Xin X Zhang Zhen Z Wang Lan L Chen Fang-Wei FW Shi Juhong J Ji Ying-Qun YQ

Respirology (Carlton, Vic.) 20220316 8

<h4>Background and objective</h4>The pulmonary embolism severity index (PESI) and simplified PESI (sPESI) are recommended to recognize patients with acute pulmonary thromboembolism (PTE) with low prognosis risk, which is of great significance for treatment. This study aims to verify the influence of hypocalcaemia on the prognosis of patients with PTE and to establish a new prognosis assessment model.<h4>Methods</h4>This is an observational, multicentre study enrolling patients with PTE from Febr ...[more]

PMID: 35297140

Similar Datasets

Project description:BackgroundComputed tomography pulmonary angiography (CTPA) is a first-line noninvasive method to diagnose acute pulmonary thromboembolism (APE); however, whether chest noncontrast CT (NC-CT) could aid in the diagnosis of APE remains unknown. The aim of this study was to build and evaluate a holistic lung graph-based machine learning (HLG-ML) using NC-CT for the diagnosis of APE and to compare its performance with that of radiologists and the YEARS algorithm.MethodsThis study enrolled 178 cases (77 males; age 63.9±16.7 years) who underwent NC-CT and CTPA in the same day from January 2019 to December 2020. Of these patients, 133 (75% of cases; 58 males; age 65.4±15.6 years) were placed into a training group and 45 (25% of cases; 19 males; age 59.6±19.2 years) into a testing group. The other 43 cases (18 males; age 62.8±20.0 years) were used to externally validate the model between January 2021 and March 2022. A HLG was developed with a pulmonary radiomics descriptor derived from NC-CT images. The approach extracted local radiomics features and encoded these local features into a radiomics descriptor as a characterization of global radiomics feature distribution. Subsequently, 8 ML models were trained and compared based on the radiomics descriptor. In the validation group, area under the curves (AUCs) of the HLG-ML model in the diagnosis of APE were compared with those of the 3 radiologists and the YEARS algorithm.ResultsAmong the 8 ML models, gradient boosting decision tree demonstrated the best classification performance (AUC =0.772) on the training set. In the testing set, the AUC of gradient boosting decision trees was 0.857 [95% confidence intervals (CIs): 0.699-0.951]. In the validation set, the performance of gradient boosting decision tree (AUC =0.810; 95% CI: 0.669-0.952; Youden index =0.621) outperformed 3 radiologists (AUC =0.508, 95% CI: 0.335-0.681, Youden index =0.016; AUC =0.504, 95% CI: 0.354-0.654, Youden index =0.008; AUC =0.527, 95% CI: 0.363-0.691, Youden index =0.050) and the YEARS algorithm (AUC =0.618; 95% CI: 0.469-0.767; Youden index =0.237).ConclusionsCompared to all 3 radiologists and the YEARS algorithm, the proposed HLG-based gradient boosting decision tree model achieved a superior performance in the diagnosis of APE on the NC-CT and may thus serve as a valuable tool for physicians in the diagnosis of APE.

Project description:The success rate of resuscitation in cardiac arrest (CA) caused by pulmonary thromboembolism (PTE) is low. Furthermore, there are no large animal models that simulate clinical CA. The aim of this study was to establish a porcine CA model caused by PTE and to investigate the pathophysiology of CA and postresuscitation.This model was induced in castrated male pigs (30 ± 2 kg; n = 21) by injecting thrombi (10-15 ml) via the left external jugular vein. Computed tomographic pulmonary angiography (CTPA) was performed at baseline, CA, and return of spontaneous circulation (ROSC). After CTPA during CA, cardiopulmonary resuscitation (CPR) with thrombolysis (recombinant tissue plasminogen activator 50 mg) was initiated. Hemodynamic, respiratory, and blood gas data were monitored. Cardiac troponins T, cardiac troponin I, creatine kinase-MB, myoglobin, and brain natriuretic peptide (BNP) were measured by enzyme-linked immunosorbent assay. Data were compared between baseline and CA with paired-sample t-test and compared among different time points for survival animals with repeated measures analysis of variance.Seventeen animals achieved CA after emboli injection, while four achieved CA after 5-8 ml more thrombi. Nine animals survived 6 h after CPR. CTPA showed obstruction of the pulmonary arteries. Mean aortic pressure data showed occurrence of CA caused by PTE (Z = -2.803, P = 0.002). The maximal rate of mean increase of left ventricular pressure (dp/dtmax) was statistically decreased (t = 6.315, P = 0.000, variation coefficient = 0.25), and end-tidal carbon dioxide partial pressure (PetCO2) decreased to the lowest value (t = 27.240, P = 0.000). After ROSC (n = 9), heart rate (HR) and mean right ventricular pressure (MRVP) remained different versus baseline until 2 h after ROSC (HR, P = 0.036; MRVP, P = 0.027). Myoglobin was statistically increased from CA to 1 h after ROSC (P = 0.036, 0.026, 0.009, respectively), and BNP was increased from 2 h to 6 h after ROSC (P = 0.012, 0.014, 0.039, respectively).We established a porcine model of CA caused by PTE. The dp/dtmaxand PetCO2may be important for the occurrence of CA, while MRVP may be more important in postresuscitation.

Project description:ObjectiveVenous thromboembolism (VTE) is a preventable cause of hospitalization-related morbidity and mortality. VTE prevention requires accurate risk stratification. Federal agencies mandated VTE risk assessment for all hospital admissions. We have shown that the widely used Caprini (30 risk factors) and Padua (11 risk factors) VTE risk-assessment models (RAMs) have limited predictive ability for VTE when used for all general hospital admissions. Here, we test whether combining the risk factors from all 23 available VTE RAMs improves VTE risk prediction.MethodsWe analyzed data from the first hospitalizations of 1,282,014 surgical and non-surgical patients admitted to 1298 Veterans Affairs facilities nationwide between January 2016 and December 2021. We used logistic regression to predict VTE within 90 days of admission using risk factors from all 23 available VTE RAMs. Area under the receiver operating characteristic curves (AUC), sensitivity, specificity, and positive (PPV) and negative predictive values (NPV) were used to quantify the predictive power of our models. The metrics were computed at two diagnostic thresholds that maximized (1) the value of sensitivity + specificity-1; and (2) PPV and were compared using McNemar's test. The Delong-Delong test was used to compare AUCs.ResultsAfter excluding those with missing data, 1,185,633 patients (mean age, 66 years; 93% male; and 72% White) were analyzed, of whom 33,253 (2.8%) had a VTE (deep venous thrombosis [DVT], n = 19,218, 1.6%; pulmonary embolism [PE], n = 10,190, 0.9%; PE + DVT, n = 3845, 0.3%). Our composite RAM included 102 risk factors and improved prediction of VTE compared with the Caprini RAM risk factors (AUC composite model: 0.74; AUC Caprini risk-factor model: 0.63; P < .0001). When the sum of sensitivity and specificity-1 was maximized, the composite model demonstrated small improvements in sensitivity, specificity and PPV; NPV was high in both models. When PPV was maximized, the PPV of the composite model was improved but remained low. The nature of the relationship between NPV and PPV precluded any further gain in PPV by sacrificing NPV and sensitivity.ConclusionsUsing a composite of 102 risk factors from all available VTE RAMs, we improved VTE prediction in a large, national cohort of >1 million general hospital admissions. However, neither model has a sensitivity or PPV that permits it to be a reliable predictor of VTE. We demonstrate the limits of currently available VTE risk prediction tools; no available RAM is ready for widespread use in the general hospital population.

Dataset Information

Prognosis assessment model based on low serum calcium in patients with acute pulmonary thromboembolism.

Background and objective

Methods

Results

Conclusion

Publications

Prognosis assessment model based on low serum calcium in patients with acute pulmonary thromboembolism.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets