Study on the Analysis of Near-Miss Ship Collisions Using Logistic Regression

2017 ◽  
Vol 21 (3) ◽  
pp. 467-473 ◽  
Author(s):  
Kwang-Il Kim ◽  
◽  
Jung Sik Jeong ◽  
Byung-Gil Lee ◽  
Keyword(s):  
Risk Assessment ◽  
Logistic Regression ◽  
Regression Analysis ◽  
High Risk ◽  
High Frequency ◽  
Near Miss ◽  
Collision Risk ◽  
Undesired Event ◽  
Accident Data ◽  
Ship Collision

Generally, risk assessment for a ship collision can be performed by analyzing the trajectories of two ships as they get close to each other. A near-miss collision between ships is an undesired event that did not result in collision, but had a high risk of doing so. Due to the high frequency of these occurrences, many actual accident data samples can be obtained. In this paper, we extract various variables related to near-miss collisions from this data, such as Distance to Closest Point of Approach (DCPA), Time to Closest Point of Approach (TCPA) and Collision Avoidance Variance (CAV). To assess near-miss collision risk, logistic regression analysis is performed by categorizing encounter types based on ship trajectories collected over 4 months in coastal water areas.

scholarly journals Improving Near Miss Detection in Maritime Traffic in the Northern Baltic Sea from AIS Data

2021 ◽  
Vol 9 (2) ◽  
pp. 180
Author(s):  
Lei Du ◽  
Osiris A. Valdez Banda ◽  
Floris Goerlandt ◽  
Pentti Kujala ◽  
Weibin Zhang
Keyword(s):  
Baltic Sea ◽  
Perceived Risk ◽  
Maritime Transportation ◽  
Near Miss ◽  
Automatic Identification ◽  
Identification System ◽  
Collision Risk ◽  
Ship Collision ◽  
The Impact ◽  
Ship Behavior

Ship collision is the most common type of accident in the Northern Baltic Sea, posing a risk to the safety of maritime transportation. Near miss detection from automatic identification system (AIS) data provides insight into maritime transportation safety. Collision risk always triggers a ship to maneuver for safe passing. Some frenetic rudder actions occur at the last moment before ship collision. However, the relationship between ship behavior and collision risk is not fully clarified. Therefore, this work proposes a novel method to improve near miss detection by analyzing ship behavior characteristic during the encounter process. The impact from the ship attributes (including ship size, type, and maneuverability), perceived risk of a navigator, traffic complexity, and traffic rule are considered to obtain insights into the ship behavior. The risk severity of the detected near miss is further quantified into four levels. This proposed method is then applied to traffic data from the Northern Baltic Sea. The promising results of near miss detection and the model validity test suggest that this work contributes to the development of preventive measures in maritime management to enhance to navigational safety, such as setting a precautionary area in the hotspot areas. Several advantages and limitations of the presented method for near miss detection are discussed.

Towards a Model of Regional Vessel Near-miss Collision Risk Assessment for Open Waters based on AIS Data

2019 ◽  
Vol 72 (06) ◽  
pp. 1449-1468 ◽  
Author(s):  
Weibin Zhang ◽  
Xinyu Feng ◽  
Yong Qi ◽  
Feng Shu ◽  
Yijin Zhang ◽  
...  
Keyword(s):  
Risk Assessment ◽  
Safety Management ◽  
Open Water ◽  
Water Area ◽  
Assessment System ◽  
Near Miss ◽  
Collision Risk ◽  
Accurate Analysis ◽  
Open Water Area ◽  
Temporal Features

The absence of a regional, open water vessel collision risk assessment system endangers maritime traffic and hampers safety management. Most recent studies have analysed the risk of collision for a pair of vessels and propose micro-level risk models. This study proposes a new method that combines density complexity and a multi-vessel collision risk operator for assessing regional vessel collision risk. This regional model considers spatial and temporal features of vessel trajectory in an open water area and assesses multi-vessel near-miss collision risk through danger probabilities and possible consequences of collision risks via four types of possible relative striking positions. Finally, the clustering method of multi-vessel encountering risk, based on the proposed model, is used to identify high-risk collision areas, which allow reliable and accurate analysis to aid implementation of safety measures.

scholarly journals Risk Factors for Leukemic Transformation Among 1,306 Patients with Primary Myelofibrosis: Mutations Predict Early Events

Blood ◽  
2018 ◽  
Vol 132 (Supplement 1) ◽  
pp. 3044-3044
Author(s):  
Rangit Reddy Vallapureddy ◽  
Mythri Mudireddy ◽  
Natasha Szuber ◽  
Domenico Penna ◽  
Maura Nicolosi ◽  
...  
Keyword(s):  
Risk Factors ◽  
Logistic Regression ◽  
Regression Analysis ◽  
High Risk ◽  
Cox Regression ◽  
Primary Myelofibrosis ◽  
Idh1 Mutation ◽  
Severe Anemia ◽  
Cox Regression Analysis ◽  
Version 2.0

Abstract Background: Current prognostic models in primary myelofibrosis (PMF) target overall survival (OS) and utilize MIPSS70 (mutation-enhanced international prognostic scoring system for transplant-age patients), MIPSS70+ version 2.0 (karyotype-enhanced MIPSS70) and GIPSS (genetically-inspired prognostic scoring system, which is based on mutations and karyotype) (JCO 2018;36:310; JCO doi: 10.1200/JCO.2018.78.9867; Leukemia. 2018;doi:10.1038/s41375-018-0107). In the current study, we used logistic regression statistics to identify risk factors for leukemic transformation (LT) within 5 years of diagnosis/referral (i.e. early events) and also performed Cox regression analysis of overall leukemia-free survival (LFS). Methods : Study patients were recruited from the Mayo Clinic, Rochester, MN, USA. Diagnoses of LT and chronic phase PMF were confirmed by both clinical and bone marrow examinations, in line with the 2016 World Health Organization criteria (Blood. 2016;127:2391); specifically, LT required presence of ≥20% blasts in the peripheral blood (PB) or bone marrow (BM) (Blood 2016;127:2391). Statistical analyses considered clinical and laboratory data collected at the time of initial PMF diagnosis or Mayo Clinic referral point. Logistic regression statistics was used to identify predictors of LT at 5 years from initial diagnosis/referral; in the particular method, patients with documented LT within 5 years were "uncensored" while those followed up for at least 5 years, without developing LT, were "censored"; the analysis excluded patients without LT and not followed for at least 5 years. In addition, Cox regression analysis was performed to identify risk factors for overall LFS. The JMP® Pro 13.0.0 software from SAS Institute, Cary, NC, USA, was used for all calculations. Results: 1,306 patients with PMF (median age 65 years; 63% males) were included in the current study; MIPSS70+ version 2.0 risk distribution was 20% very high risk, 41% high risk, 19% intermediate risk, 16% low risk and 4% very low risk. 149 (11%) patients were documented to experience LT, and compared to the remaining patients (n=1157), they were more likely to be males (p=0.02) and mutated for ASXL1 (p=0.01), SRSF2 (0.001) and IDH1 (0.02) and present with higher risk MIPSS70+ version 2.0 (p=0.02). Multivariable logistic regression identified the following as predictors of LT in the first 5 years of disease: IDH1 mutation (odds ratio; OR 78.4), very high risk (VHR) karyotype (OR 57.6), ASXL1 mutation (OR 15.1), age >70 years (OR 13.3), SRSF2 mutation (OR 8.5), male sex (OR 6.9), PB blasts ≥3% (OR 5.4), presence of moderate or severe anemia, adjusted for sex (OR 3.6) and constitutional symptoms (OR 3.1). On Cox regression analysis, the following were associated with inferior LFS: IDH1 mutation (HR 4.3), PB blasts ≥3% (HR 3.3), SRSF2 mutation (HR 3.0), age >70 years (HR 2.1), ASXL1 mutation (HR 2.0) and presence of moderate or severe anemia, adjusted for sex (HR 1.9). Subsequently, HR-based risk point allocation resulted in highly discriminating LT predictive model with HR (95% CI) of 39.4 (10.8-114) for high risk and 4.1 (2.4-7.3) for intermediate risk (Figure 1). Conclusions: The current study identifies IDH1 mutation as a main predictor of LT in PMF. Our study also implicates SRSF2 and ASXL1 mutations and VHR karyotype as other genetic markers of early LT. Other independent contributors of early LT and inferior LFS, overall, included PB blasts ≥3%, moderate to severe anemia and older age. We provide LT prediction model, based on these variables, with leukemia risk ranging from 8% to 57%. Disclosures No relevant conflicts of interest to declare.

scholarly journals Cancer Patients at Risk for Medication-Related Osteonecrosis of the Jaw. A Case and Control Study Analyzing Predictors of MRONJ Onset

2021 ◽  
Vol 10 (20) ◽  
pp. 4762
Author(s):  
Antonia Marcianò ◽  
Ylenia Ingrasciotta ◽  
Valentina Isgrò ◽  
Luca L’Abbate ◽  
Saveria Serena Foti ◽  
...  
Keyword(s):  
Risk Factors ◽  
Risk Assessment ◽  
Logistic Regression ◽  
Regression Analysis ◽  
Zoledronic Acid ◽  
Cancer Patients ◽  
Logistic Regression Analysis ◽  
Osteonecrosis Of The Jaw ◽  
P Value ◽  
Multiple Variables

The goal of this investigation was to identify potential risk factors to predict the onset of medication-related osteonecrosis of the jaw (MRONJ). Through the identification of the multiple variables positively associated to MRONJ, we aim to write a paradigm for integrated MRONJ risk assessment built on the combined analysis of systemic and local risk factors. The characteristics of a cohort of cancer patients treated with zoledronic acid and/or denosumab were investigated; beyond the set of proven risk factors a new potential one, the intake of new molecules for cancer therapy, was addressed. Registered data were included in univariate and multivariate logistic regression analysis in order to individuate significant independent predictors of MRONJ; a propensity score-matching method was performed adjusting by age and sex. Univariate logistic regression analysis showed a significant effect of the parameters number of doses of zoledronic acid and/or denosumab (OR = 1.03; 95% CI = 1.01–1.05; p = 0.008) and chemotherapy (OR = 0.35; 95% CI = 0.17–0.71; p = 0.008). The multiple logistic regression model showed that breast, multiple myeloma, and prostate cancer involved a significantly higher risk compared to lung cancer; a significant effect of the combined variables number of doses of zoledronic acid and/or denosumab (OR = 1.03; 95% CI = 1.01–1.06); p-value = 0.03) and exposure to novel molecule treatment (OR = 34.74; 95% CI = 1.39–868.11; p-value = 0.03) was observed. The results suggest that a risk assessment paradigm is needed for personalized prevention strategies in the light of patient-centered care.

Risk factors for severe anemia requiring red blood cell transfusions in lymphoma patients receiving chemotherapy

2007 ◽  
Vol 25 (18_suppl) ◽  
pp. 19537-19537
Author(s):  
X. Zhou ◽  
S. R. Teegala ◽  
A. Huen ◽  
Y. Ji ◽  
L. E. Fayad ◽  
...  
Keyword(s):  
Risk Factors ◽  
Logistic Regression ◽  
Regression Analysis ◽  
High Risk ◽  
Significant Risk ◽  
Baseline Risk ◽  
Severe Anemia ◽  
Nodal Involvement ◽  
Initial Regimen ◽  
Number Of Cycles

19537 Background: Anemia is a frequent complication in lymphoma pts receiving chemotherapy (CT). However, the exact incidence of anemia with the current regimens and risk factors for severe anemia are not well established. Methods: A retrospective cohort study was conducted to determine the incidence of anemia requiring transfusions (Tx). Medical records of all newly referred lymphoma pts (n=1046) in 2003 were reviewed. Logistic regression analysis was performed to identify the clinical and laboratory features correlated with anemia in lymphoma pts during initial regimen received. Results: 425 pts who received ≥ 1 cycle of treatment at MDACC were included in this analysis. Median age was 57 (range 17–87) with 262 (62%) newly diagnosed. Most common first regimens were CHOP (29%), Hyper-CVAD ± Ara-c-MTX (23%), and ABVD (8%) (± rituxan- R).The total number of cycles 1638 (median 3, range, 1–10). The incidence of anemia requiring PRBC Tx was 32 % (136/425) of pts and 14% (231/1638) of cycles (median cycle-2 for Tx). The incidence of PRBC Tx ranged from 8% to 17 % in each cycle. The incidence of PRBC Tx among most common regimens were Hyper-CVAD/ Ara-c/MTX 69 %( 66/95), CHOP 23% (29/125), and ABVD 6% (2/34). In the univariate regression analysis, CT, stage, extranodal/BM involvement, histology, Hb, Ca, β2M, LDH, WBC/lymphocyte counts, were significantly associated with the Txs. Using multivariate logistic regression, baseline Hb (< 12 g/dL vs. ≥ 12 g/dL: OR 2.659, 95% CI 1.670 to 4.232, p< 0.0001), extra nodal involvement (± : OR 2.578, 95% CI 1.609 to 4.133, p<0.0001), and CT (high vs low risk: OR 3.889, 95% CI 2.446 to 6.183, p<0.0001) were the most important baseline risk factors for PRBC Txs. Conclusions: The incidence of anemia in this population is high in early cycles. Baseline pt characteristics including Hb (<12g/dL), extra nodal involvement, and high risk CT were found to be significant risk factors predictive for anemia and Txs. These findings could be useful to identify high risk pts for consideration of prophylaxis with erythropoietic agents for prevention of anemia. No significant financial relationships to disclose.

Identifying the Outliers Among Gene Expression Profiling (GEP)-Defined Low-Risk Myeloma Patients Treated with Total Therapy 2 and 3 (TT2, TT3)

Blood ◽  
2012 ◽  
Vol 120 (21) ◽  
pp. 195-195
Author(s):  
Frits van Rhee ◽  
Sarah Waheed ◽  
Saad Z Usmani ◽  
Joshua Epstein ◽  
Adam Rosenthal ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Regression Analysis ◽  
High Risk ◽  
Logistic Regression Analysis ◽  
Conflicts Of Interest ◽  
Low Risk ◽  
P Value ◽  
Cytogenetic Abnormalities ◽  
Stepwise Selection ◽  
Cut Point

Abstract Abstract 195 GEP analysis is a robust method to distinguish low- and high-risk multiple myeloma (MM), pertaining to 85% and 15% of newly diagnosed patients, respectively (Shaughnessy et al., Blood, 2007; 109:2276–84). As developed in TT2 and validated in TT3A and TT3B, we are now examining, similar to previous work in high-risk MM, whether we can define outliers among low-risk MM, i.e., patients not living up to the low-risk prediction model. Toward this end, we scrutinized early relapses in TT3A and TT3B within three years of protocol entry. Using logistic regression analysis, we identified baseline parameters including GEP, en route for distinguishing this high-risk subset among low-risk MM. Also examined was whether a new model could be built within low-risk disease that allowed for the identification of a high-risk subset. Our database was interrogated for patients known to have GEP-defined low-risk in the GEP-70 model. Table 1 summarizes the 3-year events among GEP-70 low-risk subjects per protocol. An optimal cut-point at +0.146 distinguished, among the combined TT2 and TT3 patients, inferior progression-free survival (PFS) and overall survival (OS) (Figure 1a). Next, we examined outcomes among all TT2 and TT3 patients with GEP data, including those with traditionally-defined high-risk (>=0.66). Here, we were able to distinguish three subgroups with distinctly different PFS and OS (Figure 1b). Utilizing logistic regression analysis, limited to traditionally-defined GEP-70 low-risk MM (=<0.66), three-year progression events during the this period were adversely dominated by the following: GEP-70 scores >0.146 (HR=2.61, p=0.0005), the presence of cytogenetic abnormalities (CA) (HR=1.93, p=0.018), B2M >5.5mg/L (HR=1.95, p=0.04) and LDH >190U/L (HR=1.93, 0.02). These are all reported in Table 2. In conclusion, we have identified, within GEP-70 low-risk patients, a new cut-point. This allows a better categorization of patients having truly low risk disease. Also, above which a prognosis intermediate to the traditional high-risk prognostic group (>=0.66) could be identified. GEP >0.146 dominated a multivariate logistic regression model. Further efforts will be presented on unique genes characterizing this intermediate risk group in relationship to low and high-risk subsets. Table 1. Three-year Events Among GEP-70 Subjects Per Protocol Protocol Total with GEP GEP-70 low-risk GEP-70 low-risk, event within first 3 years TT2 - thalidomide 176 156 55 TT2 + thalidomide 175 149 36 TT3A 275 235 39 TT3B 166 129 23 Table 2. Logistic Regression for 3-year Event Factors, TT2+3 GEP-70 Low-Risk (<0.66) Event in first three years on protocol Variable N With Factor Without Factor OR (95% CI) P - value Multivariate B2M > 5.5 mg/L 666 24/69 (35%) 59/328 (18%) 1.95 (1.03, 3.69) 0.0401 LDH >= 190 U/L 668 33/99 (33%) 50/298 (17%) 1.93 (1.10, 3.40) 0.0229 Cytogenetic abnormalities 665 35/116 (30%) 48/281 (17%) 1.93 (1.12, 3.32) 0.0182 GEP-70 score > 0.146 669 37/104 (36%) 46/293 (16%) 2.61 (1.52, 4.47) 0.0005 OR - Odds Ratio, 95% CI - 95% Confidence Interval, P - value from Wald Chi - Square Test in Logistic Regression. NS2 - Multivariate results not statistically significant at 0.05 level. Univariate p - values reported regardless of significance. Multivariate model uses stepwise selection with entry level 0.1 and variable remains if meets the 0.05 level. A multivariate p - value greater than 0.05 indicates variable forced into model with significant variables chosen using stepwise selection. Disclosures: No relevant conflicts of interest to declare.

Sign in / Sign up

Export Citation Format

Share Document