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.

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.

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.

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 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.

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.

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.

A scoring system to predict the elevation of mean pulmonary arterial pressure in idiopathic pulmonary fibrosis.

Elevated mean pulmonary arterial pressure (MPAP; ≥21 mmHg) is sometimes seen in patients with idiopathic pulmonary fibrosis (IPF) and has an adverse impact upon survival. Although early diagnosis is crucial, there is no established screening tool that uses a combination of noninvasive examinations.We retrospectively analysed IPF patients at initial evaluation from April 2007 to July 2015 and, using logistic regression analysis, created a screening tool to identify elevated MPAP. Internal validation was also assessed for external validity using a bootstrap method.Using right-heart catheterisation (RHC), elevation of MPAP was determined to be present in 55 out of 273 patients. Multivariate models demonstrated that % predicted diffusing capacity of the lung for carbon monoxide (DLCO) <50%, ratio of pulmonary artery diameter to aorta diameter (PA/Ao) on computed tomography (CT) ≥0.9 and arterial oxygen tension (PaO2 ) <80 Torr were independent predictors. When we assigned a single point to each variable, the prevalence of elevation of MPAP with a score of zero, one, two or three points was 6.7%, 16.0%, 29.1% and 65.4%, respectively. The area under curve (AUC) for the receiver operating characteristic (ROC) curve was good at 0.757 (95% CI 0.682-0.833).A simple clinical scoring system consisting of % predicted DLCO, PA/Ao ratio on CT and PaO2 can easily predict elevation of MPAP in patients with IPF.

Multidimensional improvement in connective tissue disease-associated interstitial lung disease: Two courses of pulse dose methylprednisolone followed by low-dose prednisone and tacrolimus.

BACKGROUND AND OBJECTIVE: Corticosteroids and immunosuppressive agents are considered mainstays of therapy for connective tissue disease-related interstitial lung disease (CTD-ILD); however, tacrolimus with corticosteroid therapy has not been fully investigated. Our objectives were to examine the multidimensional therapeutic benefit and tolerability of the combined therapy for the initial treatment of patients with CTD-ILD. METHODS: In this retrospective case series, we identified consecutive CTD-ILD patients treated with tacrolimus plus intravenous (i.v.) methylprednisolone (1000 mg i.v. 3 days a week for 2 weeks) followed by low-dose prednisolone (10 mg/day). We assessed the multidimensional therapeutic benefit and tolerability including lung physiology, exercise capacity, exercise oxygen desaturation, modified Medical Research Council (MMRC) and St George's Respiratory Questionnaire (SGRQ). RESULTS: A total of 26 ILD patients with the underlying CTD diagnoses included 11 with rheumatoid arthritis, 9 with dermatomyositis, 4 with Sjögren's syndrome and 2 others. From baseline to 12 months, the combined therapy significantly improved forced vital capacity (FVC; 77.8% to 94.6%, P < 0.001), diffusing capacity of the lung for carbon monoxide (DLCO ; 66.1% to 75.1%, P < 0.001), 6-min walk distance (6MWD; 530 to 568 m, P = 0.02), lowest oxygen saturation on pulse oximetry (SpO2 ; 85% to 89%, P = 0.01), MMRC (1.3 to 0.8, P = 0.01) and SGRQ (38 to 21, P < 0.001). During the study period, only one patient's therapy was discontinued due to an adverse event and none had a life-threatening adverse event attributed to the combined therapy. CONCLUSION: In our cohort of CTD-ILD, two courses of pulse dose methylprednisolone therapy followed by prednisone and oral tacrolimus appeared to be well tolerated, and to have multidimensional efficacy.

Performance of the St George's Respiratory Questionnaire in patients with connective tissue disease-associated interstitial lung disease.

BACKGROUND AND OBJECTIVE: The St George's Respiratory Questionnaire (SGRQ) is a self-administered questionnaire used to assess health-related quality of life (HRQoL) in various chronic respiratory diseases. Few studies have assessed the performance of the SGRQ in patients with connective tissue disease-associated interstitial lung disease (CTD-ILD). We aimed to examine the SGRQ's performance characteristics and generate data to support its reliability and validity in patients with CTD-ILD. METHODS: We used data from 193 CTD-ILD patients evaluated at Tosei General Hospital from May 2007 to July 2016 to assess the cross-sectional and longitudinal validity of the SGRQ. RESULTS: The mean age of the patients was 64.2 years and 122 (63.2%) were women. There were no significant differences in SGRQ scores between any of the CTD groups. Internal consistency (Cronbach's α = 0.905) and repeatability (intraclass correlation coefficient (ICC) = 0.873) for the SGRQ total score were excellent. At baseline, SGRQ total score was significantly associated with clinically meaningful measures of physiological function, exercise capacity and dyspnoea. Change in SGRQ total score over 6 months was also associated with change in other measures. Cox proportional hazards models showed that higher baseline SGRQ total score was a significant predictor of mortality. The estimated minimal clinically important difference of SGRQ total score was 4-13 points. CONCLUSION: These data support the validity and reliability of SGRQ as a sensitive measure for capturing HRQoL in patients with CTD-ILD.

The St. George's Respiratory Questionnaire as a prognostic factor in IPF.

BACKGROUND: It is unclear whether health related quality of life (HRQL) may have a predictive value for mortality in idiopathic pulmonary fibrosis (IPF). We investigated the relationship between HRQL assessed using the St. George's Respiratory Questionnaire (SGRQ) and survival time in patients with IPF, and tried to determine a clinical meaningful cut off value to predict poorer survival rates. METHODS: We retrospectively analyzed consecutive patients with IPF who underwent an initial evaluation from May 2007 to December 2012. The diagnosis of IPF was made according to the 2011 international consensus guidelines. We used Cox proportional hazard models to identify independent predictors for mortality rate in patients with IPF. RESULTS: We examined 182 eligible cases, average age was 66 years old, and 86% were male. Mean levels of percent predicted FVC, DLco, six-minute-walk test distance, and the SGRQ total score were around 80%, 58%, 580 m, and 34 points. On multivariate analysis, the SGRQ total score (hazard ratio [HR], 1.012; 95% confidence interval [CI] 1.001-1.023; P = .029) and percent predicted FVC (HR, 0.957; 95% CI 0.944-0.971, P < .001) were independent predictors for mortality rate. Moreover, a score higher than 30 points in the SGRQ total score showed higher mortality rate (HR, 2.047; 95% CI, 1.329-3.153; P = .001). CONCLUSIONS: The SGRQ total score was one of independent prognostic factors in patients with IPF. Total scores higher than 30 points were associated with higher mortality rates. TRIAL REGISTRATION: This study was retrospective, observational study, so it is not applicable.

CT-determined pulmonary artery to aorta ratio as a predictor of elevated pulmonary artery pressure and survival in idiopathic pulmonary fibrosis.

BACKGROUND AND OBJECTIVE: Elevated mean pulmonary artery pressure (mPAP) is a significant prognostic indicator in idiopathic pulmonary fibrosis (IPF). It has been reported that the computed tomography-determined ratio of the diameter of the pulmonary artery to the aorta (PA:A) is correlated with mPAP in various respiratory diseases. However, in patients with IPF, whether the PA:A can be used to predict elevated mPAP and the prognostic value of the PA:A has not been fully evaluated. METHODS: We conducted a single-centre, observational study of 177 consecutive IPF patients who underwent right heart catheterization. We examined the association between the PA:A and mPAP in these patients, and performed a receiver operating characteristic (ROC) analysis to test the diagnostic accuracy of the PA:A in predicting mPAP > 20 mm Hg (pulmonary hypertension (PH) or borderline PH) in relation to survival. RESULTS: In a multivariate linear regression analysis, the PA:A, 6-min walk distance and diffusion capacity for carbon monoxide (DLCO ) % predicted were independent explanatory variables of elevated mPAP (P < 0.0001, P = 0.009 and P = 0.002, respectively). ROC analysis revealed a PA:A > 0.9 to be optimal for predicting mPAP > 20 mmHg (area under the curve (AUC) = 0.75; 95% CI: 0.65-0.84). Patients with a PA:A > 0.9 also had a worse prognosis (P = 0.009). CONCLUSION: Measurement of the PA:A is a useful and convenient method to predict elevated mPAP in IPF at initial evaluation. Moreover, a PA:A >0.9 was found to be an indicator of worse prognosis.

Progression of mean pulmonary arterial pressure in idiopathic pulmonary fibrosis with mild to moderate restriction.

BACKGROUND AND OBJECTIVE: Elevation of mean pulmonary arterial pressure (MPAP) is associated with poor prognosis in patients with idiopathic pulmonary fibrosis (IPF), yet the progression of MPAP in patients with IPF has not been sufficiently elucidated. We evaluated serial changes in MPAP and its determinants in patients with IPF with mild to moderate restriction. METHODS: We retrospectively reviewed patients with IPF who underwent initial evaluations including right heart catheterization (RHC) in our institute from May 2007 to December 2013 with follow-up RHC at least 1 year later. Patients with forced vital capacity (FVC) < 50% predicted or those with pulmonary artery wedge pressure >15 mm Hg were excluded. RESULTS: A total of 95 patients were included. Median follow-up time of second RHC was 1.8 years. MPAP increased significantly at follow-up (from 16.8 to 20.2 mm Hg; P < 0.001), and annual change in MPAP (ΔMPAP) was 1.8 mm Hg/year. In multiple regression analysis, the lowest oxygen saturation (SpO2 ) at 6-min walk test (6MWT) was an independent predictor of ΔMPAP. When adjusted for age, sex, baseline MPAP and FVC % predicted, ΔMPAP was a significant predictor of mortality (hazard ratio: 1.21; P = 0.001). CONCLUSION: ΔMPAP was significantly associated with desaturation in the 6MWT, and with increased mortality in patients with IPF with mild to moderate restriction.

COPD Assessment Test for measurement of health status in patients with idiopathic pulmonary fibrosis: A cross-sectional study.

BACKGROUND AND OBJECTIVE: The COPD Assessment Test (CAT) has been reported to have potential utility for measuring health status of idiopathic pulmonary fibrosis (IPF). Although the CAT has been developed for the assessment of COPD patients, it has not been fully evaluated exclusively in IPF. This study was designed to evaluate the validity of the CAT in IPF. METHODS: The clinical data for 106 consecutive subjects with newly diagnosed IPF who completed pulmonary function tests, partial pressure of oxygen in arterial blood (PaO2 ) at rest, 6-min walk test (6MWT), CAT, St George's Respiratory Questionnaire (SGRQ), modified Medical Research Council (mMRC) dyspnoea grade and Hospital Anxiety and Depression Scale (HADS), were analysed. We assessed the validity of the CAT in comparison with the SGRQ. RESULTS: The present subjects showed mild to moderate restrictive impairment on spirometry. Mean CAT score and total SGRQ were 12.8 ± 8.0 and 30.8 ± 17.7, respectively. The concurrent validity of the CAT score in comparison with the SGRQ total score was significant (r = 0.72, P < 0.001). Internal consistency (Cronbach's α = 0.869) and repeatability over 3 months (intraclass correlation coefficient = 0.742) of the CAT were also significant. Single regression analysis showed that the CAT had significant construct validity. In multiple regression analysis, mMRC, PaO2 at rest, minimum SpO2 during 6MWT and anxiety of HADS were independent predictors for the CAT. CONCLUSIONS: The CAT is a valid health status measurement in IPF patients. Multiple regression analysis showed that the CAT was significantly correlated with dyspnoea severity, oxygenation impairment and anxiety.

Disease severity staging system for idiopathic pulmonary fibrosis in Japan.

BACKGROUND AND OBJECTIVE: In Japan, the classification of disease severity of idiopathic pulmonary fibrosis (IPF) (J-system) has been used in making decisions on medical care subsidies. The present J-system consists of arterial partial pressure of oxygen (PaO2 ) and exercise desaturation in stages of I-IV. It provides a good prognostic classification in stages III and IV, but not in stages I and II. Therefore, we propose a revised system to improve discriminative ability in stages I and II. METHODS: We compared the revised J-system with the present J-system using Cox proportional hazards model to predict mortality rate. We also evaluated the recently proposed GAP (Gender, Age and Physiology) system in comparison to both J-systems. RESULTS: Two-hundred and fifteen IPF patients were studied retrospectively. A univariate model showed that the present and revised J-systems and a modified GAP system were all significant prognostic factors. The C-statistic for discriminating prognosis was higher in the revised J-system than the modified GAP system and the present J-system (0.677, 0.652 and 0.659, respectively). The C-statistics of these models produced from the 10 000 bootstrap samples were similar to those of the original models, suggesting good internal validation (0.665 (95% CI: 0.621-0.705), 0.645 (0.600-0.686) and 0.659 (0.616-0.700), respectively). Multivariate analysis revealed that the revised J-system (P = 0.0038) and the modified GAP system (P = 0.0029) were independent prognostic factors. CONCLUSION: The revised J-system can provide a better mortality prediction than the present one. Both the revised J-system and the modified GAP system are independent and valuable tools for prognostication and clinical management for IPF.

Hemosiderin-laden macrophages are an independent factor correlated with pulmonary vascular resistance in idiopathic pulmonary fibrosis: a case control study.

BACKGROUND: Increases in hemosiderin-laden macrophages (HLM) are reported to be observed in idiopathic pulmonary fibrosis (IPF). According to a recent study, significant correlation between hemosiderin deposition in the lung tissue of IPF and pulmonary hypertension evaluated by echocardiography has been suspected. In this study, we aimed to evaluate whether HLM in bronchoalveolar lavage fluid (BALF) is a factor correlated with pulmonary hemodynamic parameters evaluated by right heart catheterization in patients with IPF. METHODS: Initial data from 103 consecutive patients with IPF who underwent surgical lung biopsy between November 2007 and March 2014 were retrospectively analyzed. The "HLM score" of BALF was established by dividing the number of Perls' Prussian blue stain positive macrophages by the total number of macrophages counted. RESULTS: BALF showed an elevated HLM score (38.2%). Right heart catheterization revealed mean pulmonary arterial pressure (mPAP) of 16.3 mmHg and pulmonary vascular resistance (PVR) of 1.55 Wood units. HLM score was positively correlated with mPAP (ρ = 0.204; p = 0.038) and PVR (ρ = 0.349, p < 0.001). In multivariate analysis, 6-min walk distance (standardized partial regression coefficient [ß], -0.391; p < 0.001), minimum oxygen saturation during 6-min walk distance (ß, -0.294; p = 0.001) and HLM score (ß, 0.265; p = 0.002) were independently correlated with PVR. CONCLUSIONS: HLM score in BALF is an independent factor correlated with PVR in patients with IPF.

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.

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.