Mitochondrial DNA in bronchoalveolar lavage fluid is associated with the prognosis of idiopathic pulmonary fibrosis: a single cohort study.

BACKGROUND: Extracellular mitochondrial DNA (mtDNA) is released from damaged cells and increases in the serum and bronchoalveolar lavage fluid (BALF) of idiopathic pulmonary fibrosis (IPF) patients. While increased levels of serum mtDNA have been reported to be linked to disease progression and the future development of acute exacerbation (AE) of IPF (AE-IPF), the clinical significance of mtDNA in BALF (BALF-mtDNA) remains unclear. We investigated the relationships between BALF-mtDNA levels and other clinical variables and prognosis in IPF. METHODS: Extracellular mtDNA levels in BALF samples collected from IPF patients were determined using droplet-digital PCR. Levels of extracellular nucleolar DNA in BALF (BALF-nucDNA) were also determined as a marker for simple cell collapse. Patient characteristics and survival information were retrospectively reviewed. RESULTS: mtDNA levels in serum and BALF did not correlate with each other. In 27 patients with paired BALF samples obtained in a stable state and at the time of AE diagnosis, BALF-mtDNA levels were significantly increased at the time of AE. Elevated BALF-mtDNA levels were associated with inflammation or disordered pulmonary function in a stable state (n = 90), while being associated with age and BALF-neutrophils at the time of AE (n = 38). BALF-mtDNA ≥ 4234.3 copies/µL in a stable state (median survival time (MST): 42.4 vs. 79.6 months, p < 0.001) and ≥ 11,194.3 copies/µL at the time of AE (MST: 2.6 vs. 20.0 months, p = 0.03) were associated with shorter survival after BALF collection, even after adjusting for other known prognostic factors. On the other hand, BALF-nucDNA showed different trends in correlation with other clinical variables and did not show any significant association with survival time. CONCLUSIONS: Elevated BALF-mtDNA was associated with a poor prognosis in both IPF and AE-IPF. Of note, at the time of AE, it sharply distinguished survivors from non-survivors. Given the trends shown by analyses for BALF-nucDNA, the elevation of BALF-mtDNA might not simply reflect the impact of cell collapse. Further studies are required to explore the underlying mechanisms and clinical applications of BALF-mtDNA in IPF.

Mild elevation of pulmonary vascular resistance predicts mortality regardless of mean pulmonary artery pressure in mild interstitial lung disease.

BACKGROUND: Pulmonary hypertension (PH) is defined by elevated mean pulmonary arterial pressure (MPAP), and elevated pulmonary vascular resistance (PVR) reflects pulmonary vascular abnormalities. The clinical significance of non-severe PH in patients with various interstitial lung diseases (ILDs) has not been fully elucidated. We aimed to investigate the clinical significance of MPAP and PVR for mortality in patients with newly diagnosed ILD. METHODS: We retrospectively analysed consecutive patients with ILD at initial evaluations that included right heart catheterisation from 2007 to 2018. These patients were classified by MPAP and PVR using the 2022 the European Society of Cardiology (ESC)/the European Respiratory Society (ERS) guidelines for PH. The clinical significance of MPAP and PVR for mortality was analysed. RESULTS: Among 854 patients, 167 (19.6%) had MPAP>20 mm Hg. The proportion of patients with PVR>2 Wood units (WU) among those with MPAP≤20 mm Hg, 202 WU was associated with a higher mortality rate (HR 1.61, 95% CI 1.28 to 2.02, p<0.0001) even in a group with MPAP≤20 mm Hg. CONCLUSIONS: Mild elevation of PVR was associated with a higher mortality rate in patients with newly diagnosed ILD, even in those with MPAP≤20 mm Hg.

Predictive Models for Palliative Care Needs of Advanced Cancer Patients Receiving Chemotherapy.

CONTEXT: Early palliative care is recommended within eight-week of diagnosing advanced cancer. Although guidelines suggest routine screening to identify cancer patients who could benefit from palliative care, implementing screening can be challenging due to understaffing and time constraints. OBJECTIVES: To develop and evaluate machine learning models for predicting specialist palliative care needs in advanced cancer patients undergoing chemotherapy, and to investigate if predictive models could substitute screening tools. METHODS: We conducted a retrospective cohort study using supervised machine learning. The study included patients aged 18 or older, diagnosed with metastatic or stage IV cancer, who underwent chemotherapy and distress screening at a designated cancer hospital in Japan from April 1, 2018, to March 31, 2023. Specialist palliative care needs were assessed based on distress screening scores and expert evaluations. Data sources were hospital's cancer registry, health claims database, and nursing admission records. The predictive model was developed using XGBoost, a machine learning algorithm. RESULTS: Out of the 1878 included patients, 561 were analyzed. Among them, 114 (20.3%) exhibited needs for specialist palliative care. After under-sampling to address data imbalance, the models achieved an Area Under the Curve (AUC) of 0.89 with 95.8% sensitivity and a specificity of 71.9%. After feature selection, the model retained five variables, including the patient-reported pain score, and showcased an 0.82 AUC. CONCLUSION: Our models could forecast specialist palliative care needs for advanced cancer patients on chemotherapy. Using five variables as predictors could replace screening tools and has the potential to contribute to earlier palliative care.

Artificial intelligence in a prediction model for postendoscopic retrograde cholangiopancreatography pancreatitis.

OBJECTIVES: In this study we aimed to develop an artificial intelligence-based model for predicting postendoscopic retrograde cholangiopancreatography (ERCP) pancreatitis (PEP). METHODS: We retrospectively reviewed ERCP patients at Nagoya University Hospital (NUH) and Toyota Memorial Hospital (TMH). We constructed two prediction models, a random forest (RF), one of the machine-learning algorithms, and a logistic regression (LR) model. First, we selected features of each model from 40 possible features. Then the models were trained and validated using three fold cross-validation in the NUH cohort and tested in the TMH cohort. The area under the receiver operating characteristic curve (AUROC) was used to assess model performance. Finally, using the output parameters of the RF model, we classified the patients into low-, medium-, and high-risk groups. RESULTS: A total of 615 patients at NUH and 544 patients at TMH were enrolled. Ten features were selected for the RF model, including albumin, creatinine, biliary tract cancer, pancreatic cancer, bile duct stone, total procedure time, pancreatic duct injection, pancreatic guidewire-assisted technique without a pancreatic stent, intraductal ultrasonography, and bile duct biopsy. In the three fold cross-validation, the RF model showed better predictive ability than the LR model (AUROC 0.821 vs. 0.660). In the test, the RF model also showed better performance (AUROC 0.770 vs. 0.663, P = 0.002). Based on the RF model, we classified the patients according to the incidence of PEP (2.9%, 10.0%, and 23.9%). CONCLUSION: We developed an RF model. Machine-learning algorithms could be powerful tools to develop accurate prediction models.

Changes in patient-reported outcomes in patients with non-idiopathic pulmonary fibrosis fibrotic interstitial lung disease and progressive pulmonary fibrosis.

Background: Health-related quality of life (HRQoL) captures different aspects of the fibrotic interstitial lung disease (FILD) evaluation from the patient's perspective. However, little is known about how HRQoL changes in patients with non-idiopathic pulmonary fibrosis (IPF) FILD, especially in those with progressive pulmonary fibrosis (PPF). The aim of this study is to clarify whether HRQoL deteriorates in patients with non-IPF FILD and to evaluate the differences in the changes in HRQoL between those with and without PPF. Methods: We collected data from consecutive patients with non-IPF FILD and compared annual changes in HRQoL over 2 years between patients with PPF and those without. The St George's respiratory questionnaire (SGRQ) and COPD assessment test (CAT) were used to assess HRQoL. Changes in the SGRQ and CAT scores for 24 months from baseline were evaluated with a mixed-effect model for repeated measures. Results: A total of 396 patients with non-IPF FILD were reviewed. The median age was 65 years and 202 were male (51.0%). The median SGRQ and CAT scores were 29.6 and 11, respectively. Eighty-six (21.7%) showed PPF. Both SGRQ and CAT scores were significantly deteriorated in patients with PPF compared to those without PPF (p < 0.01 for both). Clinically important deterioration in the SGRQ and CAT scores were observed in 40.0 and 35.7% of patients with PPF and 11.7 and 16.7% of those without, respectively. PPF was significantly associated with clinically important deterioration in the SGRQ score (odds ratio 5.04; 95%CI, 2.61-9.76, p < 0.01) and CAT score (odds ratio 2.78; 95%CI, 1.27-6.06, p = 0.02). Conclusion: The SGRQ and CAT scores were significantly deteriorated in patients with non-IPF FILD and PPF. Considering an evaluation of HRQoL would be needed when assessing PPF.

In-Hospital Cancer Mortality Prediction by Multimodal Learning of Non-English Clinical Texts.

Predicting important outcomes in patients with complex medical conditions using multimodal electronic medical records remains challenge. We trained a machine learning model to predict the inpatient prognosis of cancer patients using EMR data with Japanese clinical text records, which has been considered difficult due to its high context. We confirmed high accuracy of the mortality prediction model using clinical text in addition to other clinical data, suggesting applicability of this method to cancer.

Prognostic Factors for Covid-19 on Admission Profile and Air Pollutants.

It has been reported that the severity and lethality of Covid-19 are associated with coexisting underlying diseases (hypertension, diabetes, etc.) and cardiovascular diseases (coronary artery disease, atrial fibrillation, heart failure, etc.) that increase with age, but environmental exposure such as air pollutants may also be a risk factor for mortality. In this study, we investigated patient characteristics at admission and prognostic factors of air pollutants in Covid-19 patients using a machine learning (random forest) prediction model. Age, Photochemical oxidant concentration one month prior to admission, and level of care required were shown to be highly important for the characteristics, while the cumulative concentrations of air pollutants SPM, NO2, and PM2.5 one year prior to admission were the most important characteristics for patients aged 65 years and older, suggesting the influence of long-term exposure.

Interstitial pneumonia with autoimmune features and histologic usual interstitial pneumonia treated with anti-fibrotic versus immunosuppressive therapy.

BACKGROUND: Therapeutic strategies in patients with interstitial pneumonia with autoimmune features (IPAF) and histological usual interstitial pneumonia (UIP) pattern (IPAF-UIP) have not been thoroughly evaluated. We compared the therapeutic efficacy of anti-fibrotic therapy with that of immunosuppressive treatment for patients with IPAF-UIP. METHODS: In this retrospective case series, we identified consecutive IPAF-UIP patients treated with anti-fibrotic therapy or immunosuppressive therapy. Clinical characteristics, one-year treatment response, acute exacerbation, and survival were studied. We performed a stratified analysis by the pathological presence or absence of inflammatory cell infiltration. RESULTS: Twenty-seven patients with anti-fibrotic therapy and 29 with immunosuppressive treatment were included. There was a significant difference in one-year forced vital capacity (FVC) change between patients with anti-fibrotic treatment (4 in 27 improved, 12 stable, and 11 worsened) and those with immunosuppressive treatment (16 in 29 improved, eight stable, and five worsened) (p = 0.006). There was also a significant difference in one-year St George's Respiratory Questionnaire (SGRQ) change between patients with anti-fibrotic therapy (2 in 27 improved, ten stable, and 15 worsened) and those with immunosuppressive treatment (14 in 29 improved, 12 stable, and worsened) (p < 0.001). There was no significant difference in survival between the groups (p = 0.32). However, in the subgroup with histological inflammatory cell infiltration, survival was significantly better with immunosuppressive therapy (p = 0.02). CONCLUSION: In IPAF-UIP, immunosuppressive therapy seemed to be superior to anti-fibrotic treatment in terms of therapeutic response, and provided better outcomes in the histological inflammatory subgroup. Further prospective studies are needed to clarify the therapeutic strategy in IPAF-UIP.

A comprehensible machine learning tool to differentially diagnose idiopathic pulmonary fibrosis from other chronic interstitial lung diseases.

BACKGROUND AND OBJECTIVE: Idiopathic pulmonary fibrosis (IPF) has poor prognosis, and the multidisciplinary diagnostic agreement is low. Moreover, surgical lung biopsies pose comorbidity risks. Therefore, using data from non-invasive tests usually employed to assess interstitial lung diseases (ILDs), we aimed to develop an automated algorithm combining deep learning and machine learning that would be capable of detecting and differentiating IPF from other ILDs. METHODS: We retrospectively analysed consecutive patients presenting with ILD between April 2007 and July 2017. Deep learning was used for semantic image segmentation of HRCT based on the corresponding labelled images. A diagnostic algorithm was then trained using the semantic results and non-invasive findings. Diagnostic accuracy was assessed using five-fold cross-validation. RESULTS: In total, 646,800 HRCT images and the corresponding labelled images were acquired from 1068 patients with ILD, of whom 42.7% had IPF. The average segmentation accuracy was 96.1%. The machine learning algorithm had an average diagnostic accuracy of 83.6%, with high sensitivity, specificity and kappa coefficient values (80.7%, 85.8% and 0.665, respectively). Using Cox hazard analysis, IPF diagnosed using this algorithm was a significant prognostic factor (hazard ratio, 2.593; 95% CI, 2.069-3.250; p < 0.001). Diagnostic accuracy was good even in patients with usual interstitial pneumonia patterns on HRCT and those with surgical lung biopsies. CONCLUSION: Using data from non-invasive examinations, the combined deep learning and machine learning algorithm accurately, easily and quickly diagnosed IPF in a population with various ILDs.

Prevalence and prognosis of chronic fibrosing interstitial lung diseases with a progressive phenotype.

BACKGROUND AND OBJECTIVE: The development of clinically progressive fibrosis complicates a wide array of interstitial lung diseases (ILDs). However, there are limited data regarding its prevalence and prognosis. METHODS: We analysed consecutive patients seen for initial evaluation of a fibrosing form of ILD (FILD). Patients were evaluated for evidence of progressive fibrosis over the first 24 months of follow-up. We defined a progressive phenotype as the presence of at least one of the following: a relative decline in forced vital capacity (FVC) of ≥10%; a relative decline in FVC of ≥5%-<10% with a relative decline in diffusing capacity of the lung for carbon monoxide of ≥15%, increased fibrosis on HRCT or progressive symptoms. RESULTS: Eight hundred and forty-four patients (397 with idiopathic pulmonary fibrosis [IPF] and 447 non-IPF FILD) made up the final analysis cohort. Three hundred and fifty-five patients (42.1%) met the progressive phenotype criteria (59.4% of IPF patients and 26.6% of non-IPF FILD patients, p <0.01). In both IPF and non-IPF FILD, transplantation-free survival differed between patients with a progressive phenotype and those without (p <0.01). Multivariable analysis showed that a progressive phenotype was an independent predictor of transplantation-free survival (hazard ratio [HR]: 3.36, 95% CI: 2.68-4.23, p <0.01). Transplantation-free survival did not differ between non-IPF FILD with a progressive phenotype and IPF (HR: 1.12, 95% CI: 0.85-1.48, p = 0.42). CONCLUSION: Over one-fourth of non-IPF FILD patients develop a progressive phenotype compared to approximately 60% of IPF patients. The survival of non-IPF FILD patients with a progressive phenotype is similar to IPF.

The prognostic value of the COPD Assessment Test in fibrotic interstitial lung disease.

BACKGROUND: The COPD Assessment Test (CAT) has been studied as a measure of health status in idiopathic pulmonary fibrosis (IPF) and interstitial lung disease associated with connective tissue disease. However, its prognostic value is unknown. The present study explored the association between CAT score and mortality in fibrotic interstitial lung disease (FILD), including IPF and other forms of ILD. METHODS: We retrospectively analyzed 501 consecutive patients with FILD who underwent clinical assessment, including pulmonary function test and CAT. The association between CAT score and 3-year mortality was assessed using Cox proportional hazard analysis, Kaplan-Meier plots, and the log-rank test for trend. To handle missing data, the imputed method was used. RESULTS: The patients' median age was 68 years, and 320 were male (63.9%). Regarding CAT severity, 203 patients had a low impact level (score <10), 195 had a medium level (10-20), 80 had a high level (21-30), and 23 had a very high level (31-40). During the 3-year study period, 118 patients died. After adjusting for age, sex, forced vital capacity, diffusion capacity for carbon monoxide, IPF diagnosis, and usual interstitial pneumonia pattern on high-resolution computed tomography, the CAT score was significantly associated with 3-year mortality (hazard ratio in increments of 10 points: 1.458, 95% confidence interval 1.161-1.830; p < 0.001). In addition, patients with high and very high impact levels had twofold and threefold higher mortality risk than those with low levels, respectively. CONCLUSION: The CAT has prognostic value in FILD.

The current issues and future perspective of artificial intelligence for developing new treatment strategy in non-small cell lung cancer: harmonization of molecular cancer biology and artificial intelligence.

Comprehensive analysis of omics data, such as genome, transcriptome, proteome, metabolome, and interactome, is a crucial technique for elucidating the complex mechanism of cancer onset and progression. Recently, a variety of new findings have been reported based on multi-omics analysis in combination with various clinical information. However, integrated analysis of multi-omics data is extremely labor intensive, making the development of new analysis technology indispensable. Artificial intelligence (AI), which has been under development in recent years, is quickly becoming an effective approach to reduce the labor involved in analyzing large amounts of complex data and to obtain valuable information that is often overlooked in manual analysis and experiments. The use of AI, such as machine learning approaches and deep learning systems, allows for the efficient analysis of massive omics data combined with accurate clinical information and can lead to comprehensive predictive models that will be desirable for further developing individual treatment strategies of immunotherapy and molecular target therapy. Here, we aim to review the potential of AI in the integrated analysis of omics data and clinical information with a special focus on recent advances in the discovery of new biomarkers and the future direction of personalized medicine in non-small lung cancer.

Impact of post-capillary pulmonary hypertension on mortality in interstitial lung disease.

BACKGROUND: Pulmonary hypertension (PH) influences mortality in patients with interstitial lung disease (ILD). Almost all studies on patients with ILD, have focused on the clinical impact of pre-capillary PH on survival. Therefore, little is known about the influence of post-capillary PH. We aimed to assess the prevalence of post-capillary PH and its clinical impact on survival in patients with ILD, followed by comparison with pre-capillary PH. METHODS: This retrospective study enrolled 1152 patients with ILD who were diagnosed with PH using right heart catheterization between May 2007 and December 2015. We analyzed the demographics and composite outcomes (defined as death from any cause or lung transplantation) of patients with post-capillary PH and compared them with patients with pre-capillary PH. RESULTS: Thirty-two (20%) of the 157 patients with ILD-PH were diagnosed with post-capillary PH. Patients with post-capillary PH had significantly lower modified Medical Research Council scores, higher diffusion capacity for carbon monoxide, higher resting PaO2, lower pulmonary vascular resistance (PVR), and higher lowest oxygen saturation during the 6-min walk test compared to those with pre-capillary PH. Cardiovascular diseases were associated with a higher risk of mortality in patients with post-capillary PH. Multivariate Cox proportional hazards analysis demonstrated no significant difference between the composite outcomes in pre-capillary and post-capillary PH, while PVR and the ILD Gender-Age-Physiology Index were significantly associated with the composite outcome. CONCLUSIONS: We found that approximately one-fifth of patients with ILD-PH were diagnosed with post-capillary PH, and that PVR and not post-capillary PH was associated with mortality.

Smart hospital infrastructure: geomagnetic in-hospital medical worker tracking.

PURPOSE: Location visualization is essential for locating people/objects, improving efficiency, and preventing accidents. In hospitals, Wi-Fi, Bluetooth low energy (BLE) Beacon, indoor messaging system, and similar methods have generally been used for tracking, with Wi-Fi and BLE being the most common. Recently, nurses are increasingly using mobile devices, such as smartphones and tablets, while shifting. The accuracy when using Wi-Fi or BLE may be affected by interference or multipath propagation. In this research, we evaluated the positioning accuracy of geomagnetic indoor positioning in hospitals. MATERIALS AND METHODS: We compared the position measurement accuracy of a geomagnetic method alone, Wi-Fi alone, BLE beacons alone, geomagnetic plus Wi-Fi, and geomagnetic plus BLE in a general inpatient ward, using a geomagnetic positioning algorithm by GiPStech. The existing Wi-Fi infrastructure was used, and 20 additional BLE beacons were installed. Our first experiment compared these methods' accuracy for 8 test routes, while the second experiment verified a combined geomagnetic/BLE beacon method using 3 routes based on actual daily activities. RESULTS: The experimental results demonstrated that the most accurate method was geomagnetic/BLE, followed by geomagnetic/Wi-Fi, and then geomagnetic alone. DISCUSSION: The geomagnetic method's positioning accuracy varied widely, but combining it with BLE beacons reduced the average position error to approximately 1.2 m, and the positioning accuracy could be improved further. We believe this could effectively target humans (patients) where errors of up to 3 m can generally be tolerated. CONCLUSION: In conjunction with BLE beacons, geomagnetic positioning could be sufficiently effective for many in-hospital localization tasks.

Supplemental oxygen improves exercise capacity in IPF patients with exertional desaturation.

BACKGROUND AND OBJECTIVE: The efficacy of supplemental oxygen during exercise remains unclear for patients with IPF, as there have been conflicting results from recent prospective studies with small sample sizes. METHODS: This prospective, single-blind, randomized, crossover trial evaluated the efficacy of supplemental oxygen compared with placebo air during exercise in consecutive patients with IPF without resting hypoxaemia at initial evaluation. Patients with <90% SpO2 in a 6MWT using room air were randomly assigned to a CWRET at 80% of peak work rate with oxygen or placebo air gas via nasal cannula at 4 L/min. The primary endpoint was the effect of supplemental oxygen on endurance time. RESULTS: We recruited 72 consecutive patients (median age: 66.5 years, % FVC: 84.6%, % DLCO : 61.4%). Supplemental oxygen significantly increased the endurance time (340-424 s; P < 0.001) and minimum SpO2 (88.0-94.0%; P < 0.001) compared with placebo air. Furthermore, supplemental oxygen significantly improved dyspnoea and leg fatigue. In a multivariate linear regression analysis, the endurance time on air was an independent explanatory variable of the improvement rate of endurance time (P = 0.02). CONCLUSION: In mild-moderate IPF with exercise-induced hypoxaemia even without resting hypoxaemia, supplemental oxygen during exercise improved the endurance time, desaturation and subjective symptoms. Patients with shorter endurance times with placebo air showed better improvement with supplemental oxygen.

Pulse oximetry saturation can predict prognosis of idiopathic pulmonary fibrosis.

BACKGROUND: In Japan, the severity staging system for idiopathic pulmonary fibrosis (IPF) has been used to determine medical care subsidies. However, this system requires invasive procedures to measure arterial oxygen tension. Recently, noninvasive and simple measurements of oxygen saturation by pulse oximetry (SpO2) have been used for severity assessments. We propose a pulse oximetry saturation (POS) staging system consisting of SpO2 parameters to predict prognosis. METHODS: We developed four prototype staging systems based on SpO2 at rest and desaturation, and adopted the system with the highest C-statistic as the POS staging system. The cutoff SpO2 values at rest were 96% and 90%, and desaturation was defined as SpO2 < 90% at the end of the 6-min-walk test. RESULTS: Two-hundred and nineteen IPF patients were studied and the C-statistic values of models 1, 2, 3, and 4 were 0.633, 0.643, 0.630, and 0.673, respectively. We judged model 4 to be a superior POS staging system; it defined SpO2 ≥ 96% at rest without desaturation as stage Ⅰ; SpO2 ≥ 96% at rest with desaturation or SpO2 90%-95% at rest without desaturation as stage Ⅱ; and SpO2 90%-95% at rest with desaturation or SpO2 < 90% at rest as stage Ⅲ. The hazard ratios of POS stage Ⅰ, Ⅱ, and Ⅲ were 1.00, 2.25, and 4.99, respectively. The C-statistic of the POS staging system produced from 1000 bootstrap samples was similar (0.673), suggesting good internal validation. CONCLUSION: A noninvasive and simple POS staging system defined by SpO2 can easily predict prognosis.

Probable usual interstitial pneumonia pattern on chest CT: is it sufficient for a diagnosis of idiopathic pulmonary fibrosis?

Recent studies have suggested that in patients with an idiopathic interstitial pneumonia (IIP), a probable usual interstitial pneumonia (UIP) pattern on chest computed tomography (CT) is sufficient to diagnose idiopathic pulmonary fibrosis (IPF) without histopathology.We retrospectively compared the prognosis and time to first acute exacerbation (AE) in IIP patients with a UIP and a probable UIP pattern on initial chest CT.One hundred and sixty IIP patients with a UIP pattern and 242 with a probable UIP pattern were identified. Probable UIP pattern was independently associated with longer survival time (adjusted hazard ratio 0.713, 95% CI 0.536-0.950; p=0.021) and time to first AE (adjusted hazard ratio 0.580, 95% CI 0.389-0.866; p=0.008). In subjects with a probable UIP pattern who underwent surgical lung biopsy, the probability of a histopathological UIP pattern was 83%. After multidisciplinary discussion and the inclusion of longitudinal behaviour, a diagnosis of IPF was made in 66% of cases. In IPF patients, survival time and time to first AE were not associated with CT pattern. Among subjects with a probable UIP pattern, compared to non-IPF patients, survival time and time to first AE were shorter in IPF patients.In conclusion, IIP patients with a probable UIP pattern on initial chest CT had a better prognosis and longer time to first AE than those with a UIP pattern. However, when baseline data and longitudinal behaviour provided a final diagnosis of IPF, CT pattern was not associated with these outcomes. This suggests diagnostic heterogeneity among patients with a probable UIP pattern.

Early decrease in erector spinae muscle area and future risk of mortality in idiopathic pulmonary fibrosis.

Computed tomography (CT) assessment of the cross-sectional area of the erector spinae muscles (ESMCSA) can be used to evaluate sarcopenia and cachexia in patients with lung diseases. This study aimed to confirm whether serial changes in ESMCSA are associated with survival in patients with idiopathic pulmonary fibrosis (IPF). Data from consecutive patients with IPF who were referred to a single centre were retrospectively reviewed. We measured the ESMCSA at the level of the 12th thoracic vertebra on CT images at referral and 6 months later (n = 119). The follow-up time was from 817-1633 days (median, 1335 days) and 59 patients (49.6%) died. A univariate Cox regression analysis showed that the decline in % predicted forced vital capacity (FVC) (Hazard ratios [HR] 1.041, 95% confidence interval [CI] 1.013-1.069, P = 0.004), the decline in body mass index (BMI) (HR 1.084, 95% CI 1.037-1.128; P < 0.001) and that in ESMCSA (HR 1.057, 95% CI 1.027-1.086; P < 0.001) were prognostic factors. For multivariate analyses, the decline in ESMCSA (HR 1.039, 95% CI 1.007-1.071, P = 0.015) was a significant prognostic factor, while those in % FVC and BMI were discarded. Early decrease in ESMCSA may be a useful predictor of prognosis in patients with IPF.