Combining pulmonary and cardiac computed tomography biomarkers for disease-specific risk modelling in lung cancer screening

2021 ◽  
pp. 2003386
Author(s):  
Anton Schreuder ◽  
Colin Jacobs ◽  
Nikolas Lessmann ◽  
Mireille JM Broeders ◽  
Mario Silva ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Cox Regression ◽  
Validation Cohort ◽  
External Validation ◽  
Patient Characteristics ◽  
Chronic Obstructive ◽  
Final Model ◽  
Derivation Cohort ◽  
Copd Mortality ◽  
Cvd Mortality

ObjectivesCombined assessment of cardiovascular disease (CVD), chronic obstructive pulmonary disease (COPD), and lung cancer (LC) may improve the effectiveness of LC screening in smokers. The aims were to derive and assess risk models for predicting LC incidence, CVD mortality, and COPD mortality by combining quantitative CT measures from each disease, and to quantify the added predictive benefit of self-reported patient characteristics given the availability of a CT scan.MethodsA survey model (patient characteristics only), CT model (CT information only), and final model (all variables) were derived for each outcome using parsimonious Cox regression on a sample from the National Lung Screening Trial (n=15 000). Validation was performed using Multicentric Italian Lung Detection data (n=2287). Time-dependent measures of model discrimination and calibration are reported.ResultsAge, mean lung density, emphysema score, bronchial wall thickness, and aorta calcium volume are variables which contributed to all final models. Nodule features were crucial for LC incidence predictions but did not contribute to CVD and COPD mortality prediction. In the derivation cohort, the LC incidence CT model had a 5-year area under the receiver operating characteristic curve (AUC) of 82·5% (95% confidence interval=80·9–84·0%), significantly inferior to that of the final model (84·0%, 82·6–85·5%). However, the addition of patient characteristics did not improve the LC incidence model performance in the validation cohort (CT model=80·1%, 74·2–86·0%; final model=79·9, 73·9–85·8%). Similarly, the final CVD mortality model outperformed the other two models in the derivation cohort (survey model=74·9%, 72·7–77·1%; CT model=76·3%, 74·1–78·5%; final model=79·1%, 77·0–81·2%) but not the validation cohort (survey model=74·8%, 62·2–87·5%; CT model=72·1%, 61·1–83·2%; final model=72·2%, 60·4–84·0%). Combining patient characteristics and CT measures provided the largest increase in accuracy for the COPD mortality final model (92·3%, 90·1–94·5%) compared to either other model individually (survey model=87·5%, 84·3–90·6%; CT model=87·9%, 84·8–91·0%), but no external validation was performed due to a very low event frequency.ConclusionsCT measures of CVD and COPD provides small but reproducible improvements to nodule-based LC risk prediction accuracy from 3 years’ onwards. Self-reported patient characteristics may not be of added predictive value when CT information is available.

scholarly journals Derivation and External Validation of a Simple Prediction Rule for the Development of Respiratory Failure in Hospitalized Patients With Influenza

2022 ◽  
Author(s):  
Blanca Ayuso ◽  
Antonio Lalueza ◽  
Estibaliz Arrieta ◽  
Eva Maria Romay ◽  
Álvaro Marchán-López ◽  
...  
Keyword(s):  
Respiratory Failure ◽  
Lactate Dehydrogenase ◽  
Respiratory Rate ◽  
Validation Cohort ◽  
Influenza Infection ◽  
External Validation ◽  
Prediction Rule ◽  
Influenza Viruses ◽  
Chronic Obstructive ◽  
Derivation Cohort

Abstract BACKGROUND: Influenza viruses cause seasonal epidemics worldwide with a significant morbimortality burden. Clinical spectrum of Influenza is wide, being respiratory failure (RF) one of its most severe complications. This study aims to elaborate a clinical prediction rule of RF in hospitalized Influenza patients.METHODS: a prospective cohort study was conducted during two consecutive Influenza seasons (December 2016 - March 2017 and December 2017 - April 2018) including hospitalized adults with confirmed A or B Influenza infection. A prediction rule was derived using logistic regression and recursive partitioning, followed by internal cross-validation. External validation was performed on a retrospective cohort in a different hospital between December 2018 - May 2019. RESULTS: Overall, 707 patients were included in the derivation cohort and 285 in the validation cohort. RF rate was 6.8% and 11.6%, respectively. Chronic obstructive pulmonary disease, immunosuppression, radiological abnormalities, respiratory rate, lymphopenia, lactate dehydrogenase and C-reactive protein at admission were associated with RF. A four category-grouped seven point-score was derived including radiological abnormalities, lymphopenia, respiratory rate and lactate dehydrogenase. Final model area under the curve was 0.796 (0.714-0.877) in the derivation cohort and 0.773 (0.687-0.859) in the validation cohort (p<0.001 in both cases). The predicted model showed an adequate fit with the observed results (Fisher’s test p>0.43). CONCLUSION: we present a simple, discriminating, well-calibrated rule for an early prediction of the development of RF in hospitalized Influenza patients, with proper performance in an external validation cohort. This tool can be helpful in patient´s stratification during seasonal Influenza epidemics.

scholarly journals A Computed Tomography Radiomics-Based Prediction Model on Interstitial Lung Disease in Anti-MDA5-Positive Dermatomyositis

2021 ◽  
Vol 8 ◽  
Author(s):  
Wenwen Xu ◽  
Wanlong Wu ◽  
Yu Zheng ◽  
Zhiwei Chen ◽  
Xinwei Tao ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Interstitial Lung Disease ◽  
Prediction Model ◽  
Lung Disease ◽  
Cox Regression ◽  
Validation Cohort ◽  
External Validation ◽  
High Resolution Computed Tomography ◽  
Internal Validation ◽  
Derivation Cohort

Objectives: Anti-melanoma differentiation-associated gene 5-positive dermatomyositis-associated interstitial lung disease (MDA5+ DM-ILD) is a life-threatening disease. The current study aimed to quantitatively assess the pulmonary high-resolution computed tomography (HRCT) images of MDA5+ DM-ILD by applying the radiomics approach and establish a multidimensional risk prediction model for the 6-month mortality.Methods: This retrospective study was conducted in 228 patients from two centers, namely, a derivation cohort and a longitudinal internal validation cohort in Renji Hospital, as well as an external validation cohort in Guangzhou. The derivation cohort was randomly divided into training and testing sets. The primary outcome was 6-month all-cause mortality since the time of admission. Baseline pulmonary HRCT images were quantitatively analyzed by radiomics approach, and a radiomic score (Rad-score) was generated. Clinical predictors selected by univariable Cox regression were further incorporated with the Rad-score, to enhance the prediction performance of the final model (Rad-score plus model). In parallel, an idiopathic pulmonary fibrosis (IPF)-based visual CT score and ILD-GAP score were calculated as comparators.Results: The Rad-score was significantly associated with the 6-month mortality, outperformed the traditional visual score and ILD-GAP score. The Rad-score plus model was successfully developed to predict the 6-month mortality, with C-index values of 0.88 [95% confidence interval (CI), 0.79–0.96] in the training set (n = 121), 0.88 (95%CI, 0.71–1.0) in the testing set (n = 31), 0.83 (95%CI, 0.68–0.98) in the internal validation cohort (n = 44), and 0.84 (95%CI, 0.64–1.0) in the external validation cohort (n = 32).Conclusions: The radiomic feature was an independent and reliable prognostic predictor for MDA5+ DM-ILD.

scholarly journals Development of a machine learning model for predicting pediatric mortality in the early stages of intensive care unit admission

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Bongjin Lee ◽  
Kyunghoon Kim ◽  
Hyejin Hwang ◽  
You Sun Kim ◽  
Eun Hee Chung ◽  
...  
Keyword(s):  
Machine Learning ◽  
Intensive Care Unit ◽  
Intensive Care ◽  
Validation Cohort ◽  
External Validation ◽  
Learning Model ◽  
Derivation Cohort ◽  
Icu Admission ◽  
Early Stages ◽  
Machine Learning Model

AbstractThe aim of this study was to develop a predictive model of pediatric mortality in the early stages of intensive care unit (ICU) admission using machine learning. Patients less than 18 years old who were admitted to ICUs at four tertiary referral hospitals were enrolled. Three hospitals were designated as the derivation cohort for machine learning model development and internal validation, and the other hospital was designated as the validation cohort for external validation. We developed a random forest (RF) model that predicts pediatric mortality within 72 h of ICU admission, evaluated its performance, and compared it with the Pediatric Index of Mortality 3 (PIM 3). The area under the receiver operating characteristic curve (AUROC) of RF model was 0.942 (95% confidence interval [CI] = 0.912–0.972) in the derivation cohort and 0.906 (95% CI = 0.900–0.912) in the validation cohort. In contrast, the AUROC of PIM 3 was 0.892 (95% CI = 0.878–0.906) in the derivation cohort and 0.845 (95% CI = 0.817–0.873) in the validation cohort. The RF model in our study showed improved predictive performance in terms of both internal and external validation and was superior even when compared to PIM 3.

scholarly journals Establishment and Validation of an Integrated Model to Predict Postoperative Recurrence in Patients With Atypical Meningioma

2021 ◽  
Vol 11 ◽  
Author(s):  
Xiao-Yong Chen ◽  
Jin-Yuan Chen ◽  
Yin-Xing Huang ◽  
Jia-Heng Xu ◽  
Wei-Wei Sun ◽  
...  
Keyword(s):  
Cox Regression ◽  
Validation Cohort ◽  
Clinical Laboratory ◽  
External Validation ◽  
Postoperative Recurrence ◽  
Integrated Model ◽  
Atypical Meningioma ◽  
Tumor Diameter ◽  
Roc Curve Analysis ◽  
Training Cohort

BackgroundThis study aims to establish an integrated model based on clinical, laboratory, radiological, and pathological factors to predict the postoperative recurrence of atypical meningioma (AM).Materials and MethodsA retrospective study of 183 patients with AM was conducted. Patients were randomly divided into a training cohort (n = 128) and an external validation cohort (n = 55). Univariable and multivariable Cox regression analyses, the least absolute shrinkage and selection operator (LASSO) regression analysis, time-dependent receiver operating characteristic (ROC) curve analysis, and evaluation of clinical usage were used to select variables for the final nomogram model.ResultsAfter multivariable Cox analysis, serum fibrinogen &gt;2.95 g/L (hazard ratio (HR), 2.43; 95% confidence interval (CI), 1.05–5.63; p = 0.039), tumor located in skull base (HR, 6.59; 95% CI, 2.46-17.68; p &lt; 0.001), Simpson grades III–IV (HR, 2.73; 95% CI, 1.01–7.34; p = 0.047), tumor diameter &gt;4.91 cm (HR, 7.10; 95% CI, 2.52–19.95; p &lt; 0.001), and mitotic level ≥4/high power field (HR, 2.80; 95% CI, 1.16–6.74; p = 0.021) were independently associated with AM recurrence. Mitotic level was excluded after LASSO analysis, and it did not improve the predictive performance and clinical usage of the model. Therefore, the other four factors were integrated into the nomogram model, which showed good discrimination abilities in training cohort (C-index, 0.822; 95% CI, 0.759–0.885) and validation cohort (C-index, 0.817; 95% CI, 0.716–0.918) and good match between the predicted and observed probability of recurrence-free survival.ConclusionOur study established an integrated model to predict the postoperative recurrence of AM.

Scan-based competing death risk model for reevaluating lung cancer computed tomography screening eligibility

2021 ◽  
pp. 2101613
Author(s):  
Anton Schreuder ◽  
Colin Jacobs ◽  
Nikolas Lessmann ◽  
Mireille JM Broeders ◽  
Mario Silva ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Risk Model ◽  
External Validation ◽  
Area Under The Curve ◽  
Chronic Obstructive ◽  
Quantitative Ct ◽  
Obstructive Pulmonary Disease ◽  
National Lung Screening Trial ◽  
Number Needed To Screen ◽  
Incidence Risk

PurposeA baseline CT scan for lung cancer (LC) screening may reveal information indicating that certain LC screening participants can be screened less, and instead require dedicated early cardiac and respiratory clinical input. We aimed to develop and validate competing death (CD) risk models using CT information to identify participants with a low LC and a high CD risk.MethodsParticipant demographics and quantitative CT measures of LC, cardiovascular disease, and chronic obstructive pulmonary disease were considered for deriving a logistic regression model for predicting five-year CD risk using a sample from the National Lung Screening Trial (n=15 000). Multicentric Italian Lung Detection data was used to perform external validation (n=2287).ResultsOur final CD model outperformed an external pre-scan model (CDRAT) in both the derivation (Area under the curve=0.744 [95% confidence interval=0.727 to 0.761] and 0.677 [0.658 to 0.695], respectively) and validation cohorts (0.744 [0.652 to 0.835] and 0.725 [0.633 to 0.816], respectively). By also taking LC incidence risk into consideration, we suggested a risk threshold where a subgroup (6258/23 096, 27%) was identified with a number needed to screen to detect one LC of 216 (versus 23 in the remainder of the cohort) and ratio of 5.41 CDs per LC case (versus 0.88). The respective values in the validation cohort subgroup (774/2287, 34%) were 129 (versus 29) and 1.67 (versus 0.43).ConclusionsEvaluating both LC and CD risks post-scan may improve the efficiency of LC screening and facilitate the initiation of multidisciplinary trajectories among certain participants.

scholarly journals C-Reactive Protein (CRP) Levels in Immune Checkpoint Inhibitor Response and Progression in Advanced Non-Small Cell Lung Cancer: A Bi-Center Study

Cancers ◽  
2020 ◽  
Vol 12 (8) ◽  
pp. 2319
Author(s):  
Jakob M. Riedl ◽  
Dominik A. Barth ◽  
Wolfgang M. Brueckl ◽  
Gloria Zeitler ◽  
Vasile Foris ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Validation Cohort ◽  
External Validation ◽  
Small Cell ◽  
C Reactive Protein ◽  
Small Cell Lung ◽  
Discovery Cohort ◽  
Reactive Protein ◽  
Progression Risk ◽  
Center Study

Background: Biomarkers for predicting response to immune checkpoint inhibitors (ICI) are scarce and often lack external validation. This study provides a comprehensive investigation of pretreatment C-reactive protein (CRP) levels as well as its longitudinal trajectories as a marker of treatment response and disease outcome in patients with advanced non-small cell lung cancer (NSCLC) undergoing immunotherapy with anti PD-1 or anti PD-L1 agents. Methods: We performed a retrospective bi-center study to assess the association between baseline CRP levels and anti PD-(L)1 treatment outcomes in the discovery cohort (n = 90), confirm these findings in an external validation cohort (n = 101) and explore the longitudinal evolution of CRP during anti PD-(L)1 treatment and the potential impact of dynamic CRP changes on treatment response and disease outcome in the discovery cohort. Joint models were implemented to evaluate the association of longitudinal CRP trajectories and progression risk. Primary treatment outcomes were progression-free survival (PFS) and overall survival (OS), while the objective response rate (ORR) was a secondary outcome, respectively. Results: In the discovery cohort, elevated pretreatment CRP levels emerged as independent predictors of worse PFS (HR per doubling of baseline CRP = 1.37, 95% CI: 1.16–1.63, p < 0.0001), worse OS (HR per doubling of baseline CRP = 1.42, 95% CI: 1.18–1.71, p < 0.0001) and a lower ORR ((odds ratio (OR) of ORR per doubling of baseline CRP = 0.68, 95% CI: 0.51–0.92, p = 0.013)). In the validation cohort, pretreatment CRP could be fully confirmed as a predictor of PFS and OS, but not ORR. Elevated trajectories of CRP during anti PD-(L)1 treatment (adjusted HR per 10 mg/L increase in CRP = 1.22, 95% CI: 1.15–1.30, p < 0.0001), as well as a faster increases of CRP over time (HR per 10 mg/L/month faster increase in CRP levels = 13.26, 95% CI: 1.14–154.54, p = 0.039) were strong predictors of an elevated progression risk, whereas an early decline of CRP was significantly associated with a reduction in PFS risk (HR = 0.91, 95% CI: 0.83–0.99, p = 0.036), respectively. Conclusion: These findings support the concept that CRP should be further explored by future prospective studies as a simple non-invasive biomarker for assessing treatment benefit during anti PD-(L)1 treatment in advanced NSCLC.

scholarly journals Prediction model of bleeding after endoscopic submucosal dissection for early gastric cancer: BEST-J score

Gut ◽  
2020 ◽  
pp. gutjnl-2019-319926 ◽  
Author(s):  
Waku Hatta ◽  
Yosuke Tsuji ◽  
Toshiyuki Yoshio ◽  
Naomi Kakushima ◽  
Shu Hoteya ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
High Risk ◽  
Early Gastric Cancer ◽  
Prediction Model ◽  
Endoscopic Submucosal Dissection ◽  
Clinical Decision Making ◽  
Validation Cohort ◽  
External Validation ◽  
Derivation Cohort ◽  
Submucosal Dissection

ObjectiveBleeding after endoscopic submucosal dissection (ESD) for early gastric cancer (EGC) is a frequent adverse event after ESD. We aimed to develop and externally validate a clinically useful prediction model (BEST-J score: Bleeding after ESD Trend from Japan) for bleeding after ESD for EGC.DesignThis retrospective study enrolled patients who underwent ESD for EGC. Patients in the derivation cohort (n=8291) were recruited from 25 institutions, and patients in the external validation cohort (n=2029) were recruited from eight institutions in other areas. In the derivation cohort, weighted points were assigned to predictors of bleeding determined in the multivariate logistic regression analysis and a prediction model was established. External validation of the model was conducted to analyse discrimination and calibration.ResultsA prediction model comprised 10 variables (warfarin, direct oral anticoagulant, chronic kidney disease with haemodialysis, P2Y12 receptor antagonist, aspirin, cilostazol, tumour size >30 mm, lower-third in tumour location, presence of multiple tumours and interruption of each kind of antithrombotic agents). The rates of bleeding after ESD at low-risk (0 to 1 points), intermediate-risk (2 points), high-risk (3 to 4 points) and very high-risk (≥5 points) were 2.8%, 6.1%, 11.4% and 29.7%, respectively. In the external validation cohort, the model showed moderately good discrimination, with a c-statistic of 0.70 (95% CI, 0.64 to 0.76), and good calibration (calibration-in-the-large, 0.05; calibration slope, 1.01).ConclusionsIn this nationwide multicentre study, we derived and externally validated a prediction model for bleeding after ESD. This model may be a good clinical decision-making support tool for ESD in patients with EGC.

scholarly journals 1573. Population Pharmacokinetic Analyses for Cefepime in Adult and Pediatric Patients

2019 ◽  
Vol 6 (Supplement_2) ◽  
pp. S574-S575
Author(s):  
Jiajun Liu ◽  
Michael Neely ◽  
Jeffrey Lipman ◽  
Fekade B Sime ◽  
Jason Roberts ◽  
...  
Keyword(s):  
Rate Constant ◽  
Pediatric Patients ◽  
Validation Cohort ◽  
External Validation ◽  
Compartment Model ◽  
Volume Of Distribution ◽  
Information Criteria ◽  
Population Pharmacokinetic ◽  
Final Model ◽  
Two Compartment Model

Abstract Background Cefepime (CEF) is commonly used for adult and pediatric infections. Several studies have examined CEF’s pharmacokinetics (PK) in various populations; however, a unifying PK model for adult and pediatric subjects does not yet exist. We developed a combined population model for adult and pediatric patients and validated the model. Methods The initial model includes adult and pediatric patients with a rich cefepime sampling design. All adults received 2 g CEF while pediatric subjects received a mean of 49 (SD 5) mg/kg. One- and two-compartment models were considered as base models and were fit using a non-parametric adaptive grid algorithm within the Pmetrics package 1.5.2 (Los Angeles, CA) for R 3.5.1. Compartmental model selection was based on Akaike information criteria (AIC). Covariate relationships with PK parameters were visually inspected and mathematically assessed. Predictive performance was evaluated using bias and imprecision of the population and individual prediction models. External validation was conducted using a separate adult cohort. Results A total of 45 subjects (n = 9 adults; n = 36 pediatrics) were included in the initial PK model build and 12 subjects in the external validation cohort. Overall, the data were best described using a two-compartment model with volume of distribution (V) normalized to total body weight (TBW/70 kg) and an allometric scaled elimination rate constant (Ke) for pediatric subjects (AIC = 4,138.36). Final model observed vs. predicted plots demonstrated good fit (population R2 = 0.87, individual R2 = 0.97, Figure 1a and b). For the final model, the population median parameter values (95% credibility interval) were V0 (total volume of distribution), 11.7 L (10.2–14.6); Ke for adult, 0.66 hour−1 (0.38–0.78), Ke for pediatrics, 0.82 hour−1 (0.64–0.85), KCP (rate constant from central to peripheral compartment), 1.4 hour−1 (1.3–1.8), KPC (rate constant from peripheral to central compartment), 1.6 hour−1 (1.2–1.8). The validation cohort has 12 subjects, and the final model fit the data well (individual R2 = 0.75). Conclusion In this diverse group of adult and pediatrics, a two-compartment model described CEF PK well and was externally validated with a unique cohort. This model can serve as a population prior for real-time PK software algorithms. Disclosures All authors: No reported disclosures.

scholarly journals Risk of lung cancer in patients with gastro-esophageal reflux disease: a population-based cohort study

PeerJ ◽  
2016 ◽  
Vol 4 ◽  
pp. e2753 ◽  
Author(s):  
Chi-Kuei Hsu ◽  
Chih-Cheng Lai ◽  
Kun Wang ◽  
Likwang Chen
Keyword(s):  
Lung Cancer ◽  
Cohort Study ◽  
Reflux Disease ◽  
Cox Regression ◽  
Large Population ◽  
Population Based ◽  
Chronic Obstructive ◽  
Research Database ◽  
Esophageal Reflux

This large-scale, controlled cohort study estimated the risks of lung cancer in patients with gastro-esophageal reflux disease (GERD) in Taiwan. We conducted this population-based study using data from the National Health Insurance Research Database of Taiwan during the period from 1997 to 2010. Patients with GERD were diagnosed using endoscopy, and controls were matched to patients with GERD at a ratio of 1:4. We identified 15,412 patients with GERD and 60,957 controls. Compared with the controls, the patients with GERD had higher rates of osteoporosis, diabetes mellitus, asthma, chronic obstructive pulmonary disease, pneumonia, bronchiectasis, depression, anxiety, hypertension, dyslipidemia, chronic liver disease, congestive heart failure, atrial fibrillation, stroke, chronic kidney disease, and coronary artery disease (all P < .05). A total of 85 patients had lung cancer among patients with GERD during the follow-up of 42,555 person-years, and the rate of lung cancer was 0.0020 per person-year. By contrast, 232 patients had lung cancer among patients without GERD during the follow-up of 175,319 person-years, and the rate of lung cancer was 0.0013 per person-year. By using stepwise Cox regression model, the overall incidence of lung cancer remained significantly higher in the patients with GERD than in the controls (hazard ratio, 1.53; 95% CI [1.19–1.98]). The cumulative incidence of lung cancer was higher in the patients with GERD than in the controls (P = .0012). In conclusion, our large population-based cohort study provides evidence that GERD may increase the risk of lung cancer in Asians.

scholarly journals Intensive Care Unit Outcomes Among Patients With Lung Cancer in the Surveillance, Epidemiology, and End Results–Medicare Registry

2012 ◽  
Vol 30 (14) ◽  
pp. 1686-1691 ◽  
Author(s):  
Christopher G. Slatore ◽  
Laura M. Cecere ◽  
Jennifer L. LeTourneau ◽  
Maya E. O'Neil ◽  
Jonathan P. Duckart ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Intensive Care Unit ◽  
Mechanical Ventilation ◽  
Intensive Care ◽  
Hospital Mortality ◽  
Surgical Resection ◽  
Cox Regression ◽  
Patient Characteristics ◽  
Patient Centered ◽  
End Results

Purpose Lung cancer is the leading cause of cancer-related mortality. Intensive care unit (ICU) use among patients with cancer is increasing, but data regarding ICU outcomes for patients with lung cancer are limited. Patients and Methods We used the Surveillance, Epidemiology, and End Results (SEER) –Medicare registry (1992 to 2007) to conduct a retrospective cohort study of patients with lung cancer who were admitted to an ICU for reasons other than surgical resection of their tumor. We used logistic and Cox regression to evaluate associations of patient characteristics and hospital mortality and 6-month mortality, respectively. We calculated adjusted associations for mechanical ventilation receipt with hospital and 6-month mortality. Results Of the 49,373 patients with lung cancer admitted to an ICU for reasons other than surgical resection, 76% of patients survived the hospitalization, and 35% of patients were alive 6 months after discharge. Receipt of mechanical ventilation was associated with increased hospital mortality (adjusted odds ratio, 6.95; 95% CI, 6.89 to 7.01; P < .001), and only 15% of these patients were alive 6 months after discharge. Of all ICU patients with lung cancer, the percentage of patients who survived 6 months from discharge was 36% for patients diagnosed in 1992 and 32% for patients diagnosed in 2005, whereas it was 16% and 11% for patients who received mechanical ventilation, respectively. Conclusion Most patients with lung cancer enrolled in Medicare who are admitted to an ICU die within 6 months of admission. To improve patient-centered care, these results should guide shared decision making between patients with lung cancer and their clinicians before an ICU admission.

Sign in / Sign up

Export Citation Format

Share Document