Factors Influencing Hospitalisation of Infants for Recurrent Gastroenteritis in Western Australia

2003 ◽  
Vol 42 (03) ◽  
pp. 251-254 ◽  
Author(s):  
K. Wang ◽  
K. K. W. Yau ◽  
A. H. Lee
Keyword(s):  
Prognostic Factors ◽  
Western Australia ◽  
Linked Data ◽  
Statistical Power ◽  
Proportional Hazards ◽  
Proportional Hazards Model ◽  
Discharge Rate ◽  
Discharge Summary ◽  
Older Children ◽  
Factors Influencing

Summary Objective: To determine factors affecting length of hospitalisation of infants for recurrent gastroenteritis using linked data records from the Western Australia heath information system. Methods: A seven-year retrospective cohort study was undertaken on all infants born in Western Australia in 1995 who were admitted for gastroenteritis during their first year of life (n = 519). Linked hospitalisation records were retrieved to derive the outcome measure and other demographic variables for the cohort. Unlike previous studies that focused mainly on a single episode of gastroenteritis, the durations of successive hospitalisations were analysed using a proportional hazards model with correlated frailty to determine the prognostic factors influencing recurrent gastroenteritis. Results: Older children experienced a shorter stay with an increased discharge rate of 1.9% for each month increase in admission age. An additional comorbidity recorded in the hospital discharge summary slowed the adjusted discharge rate by 46.5%. Aboriginal infants were readmitted to hospital more frequently, and had an adjusted hazard ratio of 0.253, implying a much higher risk of prolonged hospitalisation compared to non-Aborigines. Conclusions: The use of linked hospitalisation records has the advantage of providing access to hospital-based population information in the context of medical informatics. The analysis of linked data has enabled the assessment of prognostic factors influencing length of hospitalisations for recurrent gastroenteritis with high statistical power.

scholarly journals Characterisation, identification, clustering, and classification of disease

2020 ◽  
Author(s):  
A.J. Webster ◽  
K. Gaitskell ◽  
I. Turnbull ◽  
B.J. Cairns ◽  
R. Clarke
Keyword(s):  
Risk Factors ◽  
Statistical Power ◽  
Proportional Hazards ◽  
Proportional Hazards Model ◽  
Disease Risk ◽  
Neurological Diseases ◽  
Disease Incidence ◽  
Disease Onset ◽  
Laboratory Findings ◽  
Clustering And Classification

Data-driven classifications are improving statistical power and refining prognoses for a range of respiratory, infectious, autoimmune, and neurological diseases. Studies have used molecular information, age of disease incidence, and sequences of disease onset (“disease trajectories”). Here we consider whether easily measured risk factors such as height and BMI can usefully characterise diseases in UK Biobank data, combining established statistical methods in new but rigorous ways to provide clinically relevant comparisons and clusters of disease. Over 400 common diseases were selected for study on the basis of clinical and epidemiological criteria, and a conventional proportional hazards model was used to estimate associations with 12 established risk factors. Comparing men and women, several diseases had strongly sex-dependent associations of disease risk with BMI. Despite this, a large proportion of diseases affecting both sexes could be identified by their risk factors, and equivalent diseases tended to cluster adjacently. This included 10 diseases presently classified as “Symptoms, signs, and abnormal clinical and laboratory findings, not elsewhere classified”. Many clusters are associated with a shared, known pathogenesis, others suggest likely but presently unconfirmed causes. The specificity of associations and shared pathogenesis of many clustered diseases, provide a new perspective on the interactions between biological pathways, risk factors, and patterns of disease such as multimorbidity.

scholarly journals 761Characterisation and clustering of diseases by their association with well-known risk factors

2021 ◽  
Vol 50 (Supplement_1) ◽  
Author(s):  
Anthony Webster ◽  
Kezia Gaitskell ◽  
Iain Turnbull ◽  
Ben Cairns ◽  
Robert Clarke
Keyword(s):  
Risk Factors ◽  
Multiple Testing ◽  
Statistical Power ◽  
Proportional Hazards ◽  
Proportional Hazards Model ◽  
Neurological Diseases ◽  
Disease Onset ◽  
Significant Risk ◽  
Multivariate Statistical ◽  
Age Related

Abstract Background Data-driven classifications are improving statistical power, refining prognoses, and improving our understanding of autoimmune, respiratory, infectious, and neurological diseases. Classifications have used molecular information, age of incidence, and sequences of disease onset (“disease trajectories”). Here we consider whether associations with easily-measured established risk factors such as height and BMI can usefully characterise disease. Methods UK Biobank data and their linked hospital episode statistics were used to study 172 common age-related diseases. A proportional hazards model was used to estimate associations with potential risk-factors and to adjust for well-known confounders. Diseases were compared and hierarchically clustered using novel but rigorous multivariate statistical methods. Results For diseases affecting both sexes, over 38% can be uniquely identified by their associations with risk factors. Equivalent diseases often clustered adjacently. After an FDR multiple-testing adjustment, roughly 5% have statistically significant differences. Similar remarks applied to several symptoms of unknown cause. Many clustered diseases are associated with a shared, known pathogenesis, others suggest likely but unconfirmed causes. Conclusions Risk factors for disease can be surprisingly precise and can be used to cluster diseases in a meaningful way. Risk factors for men and women may differ for some diseases. Several symptoms of unknown cause have disease-specific, statistically significant risk factors. Key messages Big datasets and modern statistics are providing new insights into the relationships between diseases and their associations with risk-factors. Diseases can be identified and clustered by their associations with well-known risk factors.

scholarly journals Survival of patients with appendiceal neuroendocrine tumors: whether they benefit from right hemicolectomy?

2020 ◽  
Author(s):  
Haiping Lin ◽  
Liyuan Wang ◽  
Yang Bai ◽  
Chenyang Ge ◽  
Hongjuan Zheng ◽  
...  
Keyword(s):  
Prognostic Factors ◽  
Propensity Score ◽  
Propensity Score Matching ◽  
Neuroendocrine Tumors ◽  
Proportional Hazards ◽  
Proportional Hazards Model ◽  
Right Hemicolectomy ◽  
Prognostic Impact ◽  
Subdistribution Hazard ◽  
Survival Difference

Abstract Background: Current guidelines recommend right hemicolectomy for appendiceal neuroendocrine tumors (A-NETs) patients with lymph node (LN) metastasis. However, prognosis of these patients is favorable, and the prognostic impact of metastatic LN is controversial.Objective: The study aims to evaluate the prognostic factors of A-NETs, and explore whether right hemicolectomy/ more extended procedure (RHCM) improves prognosis compared to less extended than right hemicolectomy (LRHC).Methods: Patients with A-NETs were identified from the Surveillance, Epidemiology, and End Results (SEER) database. The Fine-Gray proportional hazards model was established to calculate subdistribution hazard ratios of prognostic factors. A propensity score matching was performed to balance intergroup differences between the LRHC and RHCM groups, and survival difference between the after-matched groups was tested using the Gray test. Subgroup analyses were also conducted.Results: In the multivariate analysis, histological type and distant metastatic status were associated with prognosis, while tumor size and nodal status were not. After propensity score matching, the patients’ characteristics were well balanced. RHCM did not confer survival benefits in the whole after-matched patients or any subgroup.Conclusions: Metastatic LN does not significantly impact prognosis, and RHCM fails to improve prognosis compared to LRHC. Therefore, the current “LN-decided” surgical procedure may not be suitable for patients with A-NETs.

Prognostic factors for overall survival with sunitinib as first-line therapy in patients with metastatic renal cell carcinoma (mRCC)

2009 ◽  
Vol 27 (15_suppl) ◽  
pp. 5042-5042
Author(s):  
S. Patil ◽  
R. A. Figlin ◽  
T. E. Hutson ◽  
M. D. Michaelson ◽  
S. Négrier ◽  
...  
Keyword(s):  
Overall Survival ◽  
Prognostic Factors ◽  
Proportional Hazards ◽  
Proportional Hazards Model ◽  
Progression Free Survival ◽  
Cox Proportional Hazards ◽  
Cox Proportional Hazards Model ◽  
Rank Test ◽  
Type I ◽  
First Line

5042 Background: Sunitinib demonstrated superior progression-free survival (PFS; the primary endpoint) over interferon-alfa (IFN-α) as first-line mRCC therapy (NEJM 2007;356:115). Median overall survival (OS) with sunitinib compared to IFN-α was: 26.4 vs. 21.8 months (HR=0.821; P=0.051 by unstratified log-rank test; Proc ASCO 2008;26, May 20 suppl; abstr 5024). An analysis of prognostic factors for OS was performed on data from this trial. Methods: 750 treatment-naïve mRCC patients were randomized 1:1 to receive sunitinib or IFN-α. By Cox proportional hazards model, selected pretreatment variables were evaluated univariately and in a multivariate model for each treatment arm. Multivariate models for each treatment arm were based on a stepwise algorithm with a type I error of 0.25 for entry and 0.15 for elimination. Further elimination was applied to identify variables significant at P<0.05. Results: In multivariate analysis of sunitinib patients, factors associated with longer OS include: interval from diagnosis to treatment ≥1 yr, ECOG PS of 0, lower corrected calcium, absence of bone metastases, lower lactic dehydrogenase (LDH), and higher hemoglobin (Hgb) ( table ). For the IFN-α treatment arm, male gender, absence of bone or lymph node metastases, lower LDH, higher Hgb, lower corrected calcium, higher neutrophil count, and interval from diagnosis to treatment ≥1 yr were associated with longer OS. Conclusions: For patients in the sunitinib treatment arm, prognostic factors identified were similar to the factors previously identified in the MSKCC risk groups (J Clin Oncol 2002;20:289). Additional prognostic factors were identified for the IFN-α arm. Further studies are warranted to independently validate these findings as well as to identify tumor-specific prognostic factors. [Table: see text] [Table: see text]

scholarly journals Prognostic Factors in Patients with Rhabdomyosarcoma Using Competing-Risks Analysis: A Study of Cases in the SEER Database

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Didi Han ◽  
Chengzhuo Li ◽  
Xiang Li ◽  
Qiao Huang ◽  
Fengshuo Xu ◽  
...  
Keyword(s):  
Prognostic Factors ◽  
Competing Risks ◽  
Cumulative Incidence ◽  
Proportional Hazards ◽  
Proportional Hazards Model ◽  
Treatment Strategies ◽  
Incidence Rates ◽  
Seer Database ◽  
Competing Risks Analysis ◽  
Using Data

Background. Rhabdomyosarcoma (RMS) is a rare malignant soft-tissue sarcoma characterized by a poor outcome and unclear prognostic factors. This study applied a competing-risks analysis using data from the Surveillance, Epidemiology, and End Results (SEER) database to RMS patients, with the aim of identifying more accurate prognostic factors. Methods. Data of all patients with RMS during 1986–2015 were extracted from the SEER database. We used the competing-risks approach to calculate the cumulative incidence function (CIF) for death due to rhabdomyosarcoma (DTR) and death from other causes (DOC) at each time point. The Fine–Gray subdistribution proportional-hazards model was then applied in univariate and multivariate analyses to determine how the CIF differs between groups and to identify independent prognostic factors. The potential prognostic factors were analyzed using the competing-risks analysis methods in SAS and R statistical software. Results. This study included 3399 patients with RMS. The 5-year cumulative incidence rates of DTR and DOC after an RMS diagnosis were 39.9% and 8.7%, respectively. The multivariate analysis indicated that age, year of diagnosis, race, primary site, historic stage, tumor size, histology subtype, and surgery status significantly affected the probability of DTR and were independent prognostic factors in patients with RMS. A nomogram model was constructed based on multivariate models for DTR and DOC. The performances of the two models were validated by calibration and discrimination, with C-index values of 0.758 and 0.670, respectively. Conclusions. A prognostic nomogram model based on the competing-risks model has been established for predicting the probability of death in patients with RMS. This validated prognostic model may be useful when choosing treatment strategies and for predicting survival.

Overall survival (OS) prognostic factors in metastatic castrate-resistant prostate cancer (mCRPC) patients retreated with docetaxel (D).

2012 ◽  
Vol 30 (15_suppl) ◽  
pp. e15213-e15213
Author(s):  
Shilpa Rashmikant Shah ◽  
Costantine Albany ◽  
Noah M. Hahn
Keyword(s):  
Prognostic Factors ◽  
Proportional Hazards ◽  
Proportional Hazards Model ◽  
Cox Proportional Hazards ◽  
Cox Proportional Hazards Model ◽  
Cancer Center ◽  
Gleason Grade ◽  
First Line ◽  
Hazards Model ◽  
Baseline Psa

e15213 Background: Until recently, few post-D treatment options existed for mCRPC patients (pts). In pts taken off of first-line D for reasons other than progressive disease (PD), retreatment with D at the time of PD was a commonly used strategy. While several prognostic nomograms have been developed for mCRPC patients, none have exclusively examined prognostic factors in D retreated patients. In the present study, we aimed to characterize baseline clinical factors associated with OS outcomes in D retreated mCRPC pts. Methods: Between 1/2007 and 10/2010, all mCRPC pts seen in the Indiana University Simon Cancer Center oncology clinics were approached for recruitment to the IRB approved biorepository protocol. Participants completed a demographic and clinical questionnaire and provided a blood sample. Only mCRPC pts retreated with D with confirmed dates for D first-line completion, D retreatment, and OS were included in this analysis. At D retreatment, patient age, Gleason Grade (GG), presence of pain, time to progression after first-line D therapy, visceral metastases, ECOG performance status, PSA, and PSA doubling time (PSAdt) were examined by Cox proportional hazards model for significant associations (p < 0.05) with OS outcomes. Results: 30 mCRPC pts retreated with D were identified. Complete data were available on 22 pts which formed the analysis cohort. Demographics included: mean age – 69.1 yrs, Caucasian/African-American – 21/1, median GG 8, 55% pain present at baseline, median time from first-line D – 8.6 mo., 30% visceral mets, ECOG 0/1/2 – 3/17/2, mean baseline PSA – 536 ng/ml, mean baseline PSAdt – 2.7 mo. By Cox proportional hazards model, baseline PSA (p = 0.006) was significantly associated with D-retreatment OS with a trend toward significance in patients with pain at baseline (p = 0.059). No other significant associations were identified. Conclusions: Baseline PSA levels were significantly associated with OS outcomes in mCRPC pts retreated with D. Validation and examination of additional clinical variables is warranted in larger datasets.

scholarly journals Factors associated with survival in patients with meningioma

1997 ◽  
Vol 2 (4) ◽  
pp. E3 ◽  
Author(s):  
Bridget J. McCarthy ◽  
Faith Davis ◽  
Sally Freels ◽  
Tanya S. Surawicz ◽  
Denise Damek ◽  
...  
Keyword(s):  
Radiation Therapy ◽  
Prognostic Factors ◽  
Survival Rate ◽  
Program Analysis ◽  
Proportional Hazards ◽  
Proportional Hazards Model ◽  
Malignant Tumors ◽  
Survival Rates ◽  
Age At Diagnosis ◽  
Benign Tumors

Factors affecting the survival rate in patients with meningiomas were explored using the National Cancer Database (NCDB), which includes tumors from approximately 1000 hospitals participating in the American College of Surgeons tumor registry program. Analysis included over 9000 cases diagnosed from 1985 to 1988 and 1990 to 1992. Survival estimates were computed and prognostic factors were identified using a proportional hazards model. The overall 5-year survival rate was 69% and it declined with age. This rate was 81% in patients aged 21 to 64 and 56% for patients 65 years of age or older. When patients were grouped by the histological type of their tumors, those with benign tumors had an overall 5-year survival rate of 70%, whereas the overall 5-year survival rates in patients with atypical and malignant meningiomas were 75% and 55%, respectively. Prognostic factors for benign tumors included age at diagnosis, tumor size, whether treated surgically, hospital type, and radiation therapy; for malignant tumors, age at diagnosis, whether treated surgically, and radiation therapy were statistically significant. The 5-year rate for recurrence of symptoms (regardless of the method of treatment) was 18.2% for those with benign tumors and 27.5% for those with malignant tumors. In patients whose benign tumor had been completely removed, the 5-year rate of tumor recurrence was 20.5%. Although not population-based, the NCDB has the potential for providing pertinent information regarding patient characteristics and methods of treatment for benign, as well as malignant, brain tumors.

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