scholarly journals Delayed diagnosis of active pulmonary tuberculosis - potential risk factors for patient and healthcare delays in Portugal

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
João Almeida Santos ◽  
Andreia Leite ◽  
Patrícia Soares ◽  
Raquel Duarte ◽  
Carla Nunes
Keyword(s):  
Pulmonary Tuberculosis ◽  
Proportional Hazards Model ◽  
Cox Model ◽  
Delayed Diagnosis ◽  
Alcohol Problems ◽  
Patient Delay ◽  
Cox Proportional Hazards Model ◽  
Diagnosis And Treatment ◽  
Effective Control ◽  
Total Delay

Abstract Background Early diagnosis and treatment of pulmonary tuberculosis (PTB) is essential for an effective control of the tuberculosis (TB) epidemic. Delayed diagnosis and treatment of TB increases the chance of complications and mortality for the patients, and enhances TB transmission in the population. Therefore, the aim of this study was to characterize patient, healthcare and total delay in diagnosing PTB and assess the effect of clinical and sociodemographic factors on the time until first contact with healthcare or reaching a PTB diagnosis. Methods Retrospective cohort study that included active PTB patients notified in the National Tuberculosis Surveillance System (SVIG-TB), between 2008 and 2017. Descriptive statistics, Kaplan-Meier estimates, logrank test and Cox proportional hazards model were used to characterize patient, healthcare and total delay and estimate the effect of clinical and sociodemographic variables on these delays. Significance level was set at 0.05. Results Median patient, healthcare and total delays was 37 days (Interquartile range (IQR): 19–71), 8 days (IQR: 1–32) and 62 days (IQR: 38–102), respectively. The median patient delay showed a constant increase, from 33 days in 2008 to 44 days in 2017. The median total delay presented a similar trend, increasing from 59 days in 2008 to 70 days in 2017. Healthcare delay remained constant during the study period. More than half of the PTB cases (82.9%) had a delay > 1 month between symptom onset and diagnosis. In the final Cox model, alcohol abuse, unemployment and being from a high TB incidence country were factors significantly associated with longer patient delay, while being female, having more than 45 years, oncologic and respiratory diseases were associated with longer healthcare delay. Being female, having more than 45 years and being from a high TB incidence country were associated with longer total delay. Conclusions Patient delay and total delay have increased in recent years. Older patients, patients with alcohol problems, other comorbidities, unemployed or from countries with high TB incidence would benefit from the development of specific public health strategies that could help reduce the delay in TB diagnosis observed in our study. This study emphasizes the need to promote awareness of TB in the general population and among the healthcare community, especially at ambulatory care level, in order to reduce the gap between beginning of symptoms and TB diagnosis.

scholarly journals 1388. Dose Discrimination for ASN100: Bridging from Rabbit Survival Data to Predicted Activity in Humans Using a Minimal Physiologically Based Pharmacokinetic (mPBPK) Model

2018 ◽  
Vol 5 (suppl_1) ◽  
pp. S426-S426
Author(s):  
Christopher M Rubino ◽  
Lukas Stulik ◽  
Harald Rouha ◽  
Zehra Visram ◽  
Adriana Badarau ◽  
...  
Keyword(s):  
Survival Data ◽  
Proportional Hazards ◽  
Proportional Hazards Model ◽  
Cox Model ◽  
Virulent Strain ◽  
Cox Proportional Hazards ◽  
Cox Proportional Hazards Model ◽  
Simulated Patients ◽  
Research Support ◽  
Mpbpk Model

Abstract Background ASN100 is a combination of two co-administered fully human monoclonal antibodies (mAbs), ASN-1 and ASN-2, that together neutralize the six cytotoxins critical to S. aureus pneumonia pathogenesis. ASN100 is in development for prevention of S. aureus pneumonia in mechanically ventilated patients. A pharmacometric approach to dose discrimination in humans was taken in order to bridge from dose-ranging, survival studies in rabbits to anticipated human exposures using a mPBPK model derived from data from rabbits (infected and noninfected) and noninfected humans [IDWeek 2017, Poster 1849]. Survival in rabbits was assumed to be indicative of a protective effect through ASN100 neutralization of S. aureus toxins. Methods Data from studies in rabbits (placebo through 20 mg/kg single doses of ASN100, four strains representing MRSA and MSSA isolates with different toxin profiles) were pooled with data from a PK and efficacy study in infected rabbits (placebo and 40 mg/kg ASN100) [IDWeek 2017, Poster 1844]. A Cox proportional hazards model was used to relate survival to both strain and mAb exposure. Monte Carlo simulation was then applied to generate ASN100 exposures for simulated patients given a range of ASN100 doses and infection with each strain (n = 500 per scenario) using a mPBPK model. Using the Cox model, the probability of full protection from toxins (i.e., predicted survival) was estimated for each simulated patient. Results Cox models showed that survival in rabbits is dependent on both strain and ASN100 exposure in lung epithelial lining fluid (ELF). At human doses simulated (360–10,000 mg of ASN100), full or substantial protection is expected for all four strains tested. For the most virulent strain tested in the rabbit pneumonia study (a PVL-negative MSSA, Figure 1), the clinical dose of 3,600 mg of ASN100 provides substantially higher predicted effect relative to lower doses, while doses above 3,600 mg are not predicted to provide significant additional protection. Conclusion A pharmacometric approach allowed for the translation of rabbit survival data to infected patients as well as discrimination of potential clinical doses. These results support the ASN100 dose of 3,600 mg currently being evaluated in a Phase 2 S. aureus pneumonia prevention trial. Disclosures C. M. Rubino, Arsanis, Inc.: Research Contractor, Research support. L. Stulik, Arsanis Biosciences GmbH: Employee, Salary. H. Rouha, 3Arsanis Biosciences GmbH: Employee, Salary. Z. Visram, Arsanis Biosciences GmbH: Employee, Salary. A. Badarau, Arsanis Biosciences GmbH: Employee, Salary. S. A. Van Wart, Arsanis, Inc.: Research Contractor, Research support. P. G. Ambrose, Arsanis, Inc.: Research Contractor, Research support. M. M. Goodwin, Arsanis, Inc.: Employee, Salary. E. Nagy, Arsanis Biosciences GmbH: Employee, Salary.

scholarly journals Incidence and Predictors of Hospitalization in Patients with Atrial Fibrillation: Results from the Chinese Atrial Fibrillation Registry Study

2020 ◽  
Author(s):  
Zhaojie Dong ◽  
Xin Du ◽  
Shangxin Lu ◽  
Chao Jiang ◽  
Shijun Xia ◽  
...  
Keyword(s):  
Atrial Fibrillation ◽  
Cardiovascular Diseases ◽  
Proportional Hazards Model ◽  
Cox Model ◽  
Cox Proportional Hazards ◽  
Cox Proportional Hazards Model ◽  
Chronic Obstructive ◽  
Unique Identifier ◽  
Clinical Trial Registration

Abstract Background: Patients with atrial fibrillation (AF) underwent a high risk of hospitalization, which, however, has not been paid much attention in clinic. Therefore, we aimed to assess the incidence, causes and predictors of hospitalization in AF patients.Methods: From August 2011 to December 2017, 20,172 AF patients from the Chinese Atrial Fibrillation Registry (China-AF) Study were enrolled in this study. We described the incidence, causes of hospitalization according to age and gender categories. The Cox proportional hazards model was employed to identify predictors of first all-cause and first cause-specific hospitalization. Results: After a mean follow-up of 37.3 ± 20.4 months, 7,512 (37.2%) AF patients experienced one or more hospitalizations. The overall incidence of all-cause hospitalization was 24.0 per 100 patient-years. Patients aged < 65 years were predominantly hospitalized for AF (42.1% of the total frequency of hospitalizations); while patients aged 65-74 and ≥ 75 years were mainly hospitalized for non-cardiovascular diseases (43.6% and 49.3%, respectively). Multivariate Cox model analysis verified the higher risk of hospitalization in patients complicated with heart failure (HF)[hazard ratio (HR) 1.15, 95% confidence interval (CI) 1.08-1.24], established coronary artery disease (CAD) (HR 1.26, 95%CI 1.19-1.34), ischemic stroke/transient ischemic attack (TIA) (HR 1.26, 95%CI 1.18-1.33), diabetes (HR 1.16, 95%CI 1.10-1.22), chronic obstructive pulmonary disease (COPD) (HR 1.41, 95%CI 1.13-1.76), gastrointestinal disorder (HR 1.39, 95%CI 1.23-1.58), and renal dysfunction (HR 1.31, 95%CI 1.16-1.48). Conclusions: More than one-third of AF patients included in this study were hospitalized at least once during almost 3 years of follow-up. The main cause for hospitalization among elderly patients (≥65 years) is non-cardiovascular diseases rather than AF. Multidisciplinary management of comorbidities should be advocated as strategies to reduce hospitalization in AF patients.Clinical Trial Registration: URL: http://www.chictr.org.cn/showproj.aspx?proj=5831. Unique identifier: ChiCTR-OCH-13003729.

Hospital readmissions and the day of the week

2018 ◽  
Vol 23 (1) ◽  
pp. 21-27 ◽  
Author(s):  
Aman Verma ◽  
Christian Rochefort ◽  
Guido Powell ◽  
David Buckeridge
Keyword(s):  
Markov Model ◽  
Proportional Hazards Model ◽  
Cox Model ◽  
Hospital Readmissions ◽  
Cox Proportional Hazards Model ◽  
Effect Estimate ◽  
Time Varying ◽  
Confounding Effect ◽  
Day Of The Week ◽  
Emergency Readmission

Objectives Patients discharged from hospitals on a Friday (Friday discharges) are readmitted sooner (a shorter time-to-emergency-readmission) than those discharged on any other day of the week. To evaluate the cost-effectiveness of increasing weekend capacity, the effect estimate of Friday discharge on time-to-emergency-readmission needs to be precise. However, precise effect estimation is complicated by the confounding effect of differing healthcare-seeking behaviour and admission practices, and therefore different admission probability, by day of the week. The objective of this research was to examine how differing admission probability by day of the week influences the effect of discharge day on time-to-emergency-readmission. Methods We used a Markov model to determine how day of the week admission probability would theoretically affect the time-to-emergency-readmission for Friday and Wednesday discharges. We tested this in a cohort of patients who have had a history of respiratory illness, using a Cox proportional hazards model to fit the time-to-emergency-readmission to any Quebec hospital as a function of the day of the week of discharge and admission. We fitted another Cox model with an additional time-varying covariate for the current day of the week, to model differing admission probabilities by day of the week. Results Our Markov model showed that if admission probability is lower on the weekends, Friday discharges will be readmitted later (longer time-to-emergency-readmission) than Wednesday discharges. Using hospital admission data, we found that Friday discharges were readmitted slightly earlier than Wednesday discharges (HR: 1.03, 95% CI: (1.02, 1.05)). After adding a time-varying covariate for the current day of the week, the length of time-to-emergency-readmission for a Friday discharge increased, but it was still earlier than a Wednesday discharge (HR: 1.04, 95% CI: (1.01, 1.07)). Conclusions The lower admission probabilities on the weekend confound the effect of Friday discharge on time-to-emergency-readmission by increasing the time-to-emergency-readmission. This confounding effect causes an underestimate of the effect of Friday discharge on time-to-emergency-readmission.

Simulating Duration Data for the Cox Model

2018 ◽  
Vol 7 (04) ◽  
pp. 921-928 ◽  
Author(s):  
Jeffrey J. Harden ◽  
Jonathan Kropko
Keyword(s):  
Hazard Function ◽  
Proportional Hazards ◽  
Proportional Hazards Model ◽  
Cox Model ◽  
Cox Proportional Hazards ◽  
Cox Proportional Hazards Model ◽  
Simulation Studies ◽  
Baseline Hazard ◽  
Baseline Hazard Function ◽  
Time Varying Covariates

The Cox proportional hazards model is a popular method for duration analysis that is frequently the subject of simulation studies. However, no standard method exists for simulating durations directly from its data generating process because it does not assume a distributional form for the baseline hazard function. Instead, simulation studies typically rely on parametric survival distributions, which contradicts the primary motivation for employing the Cox model. We propose a method that generates a baseline hazard function at random by fitting a cubic spline to randomly drawn points. Durations drawn from this function match the Cox model’s inherent flexibility and improve the simulation’s generalizability. The method can be extended to include time-varying covariates and non-proportional hazards.

The effect of pathologic T-stage and Gleason score on cancer-specific survival for specimen-confined high-risk prostate cancer.

2014 ◽  
Vol 32 (4_suppl) ◽  
pp. 114-114
Author(s):  
Lorenzo Tosco ◽  
Hendrik Van Poppel ◽  
Thomas Van den Broeck ◽  
Patrick Bastian ◽  
Alberto Briganti ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
High Risk ◽  
Gleason Score ◽  
Proportional Hazards Model ◽  
Cox Model ◽  
Cox Proportional Hazards Model ◽  
Cancer Specific Survival ◽  
High Risk Prostate Cancer ◽  
Kaplan Meier ◽  
Risk Prostate Cancer

114 Background: High-risk prostate cancer (HRPC) is a challenging disease and the role of surgery is often considered in the context of a multimodal approach. The indication for adjuvant therapy after surgery for HRPC patients who have specimen-confined disease (R0, pN0, <pT3b) is still difficult. The current study aims to analyze postoperative pathological features which help to predict CSS in specimen-confined HRPC and thus may aid in the decision to administer adjuvant EBRT or ADT. Methods: From a multi-institutional retrospective cohort of 5876 HRPC patients treated by radical prostatectomy and pelvic lymph node dissection, 1391 patients with specimen-confined disease were selected. Following surgery, adjuvant EBRT and/or ADT were delivered according to institutional protocols. Patients were subdivided into four groups according to pT stage (pT≥3 and pT<3) and final Gleason score (GS≥8 and GS<8). Kaplan-Meier plots with log-rank tests and a Cox proportional hazards model were applied to study CSS. All significance levels were set at 0.05. MedCalc was used for all statistical analyses. Results: Median age was 65 years (43-84). Of all patients, 346 (24.9%) had GS≥8 and 794 (57.1%) had pT≥3 at definitive histopathology. Patients were classified into COMBO groups: C1 (478; 34.4%; GS<8,pT<3), C2 (567; 40.8%; GS<8, pT≥3), C3 (119; 8.6%; GS≥8, pT<3), C4 (227; 16.3%; GS≥8, pT≥3). Adjuvant EBRT and ADT, respectively, were delivered in C1 2%/2%, C2 15%/22%, C3 3%/10%, C4 18%/25%. Kaplan Meier plots demonstrated statistically different 10-yr CSS between groups: C1 97.4%, C2 95.2%, C3 89.9% and C4 84.4% (p<0.0001). COMBO groups were also compared using a Cox model and results are shown in the Table. Conclusions: COMBO groups demonstrated to be able to subdivide specimen-confined HRPC into 4 demarcated groups with significantly different CSS. This subdivision could be considered an easy-to-use tool which can help for counseling patients for adjuvant treatment strategies. [Table: see text]

Proportionally Difficult: Testing for Nonproportional Hazards in Cox Models

2010 ◽  
Vol 18 (2) ◽  
pp. 189-205 ◽  
Author(s):  
Luke Keele
Keyword(s):  
Proportional Hazards ◽  
Proportional Hazards Model ◽  
Cox Model ◽  
Standard Test ◽  
Cox Proportional Hazards ◽  
Cox Proportional Hazards Model ◽  
Cox Models ◽  
Nonproportional Hazards ◽  
Specification Errors ◽  
Hazards Model

The Cox proportional hazards model is widely used to model durations in the social sciences. Although this model allows analysts to forgo choices about the form of the hazard, it demands careful attention to the proportional hazards assumption. To this end, a standard diagnostic method has been developed to test this assumption. I argue that the standard test for nonproportional hazards has been misunderstood in current practice. This test detects a variety of specification errors, and these specification errors must be corrected before one can correctly diagnose nonproportionality. In particular, unmodeled nonlinearity can appear as a violation of the proportional hazard assumption for the Cox model. Using both simulation and empirical examples, I demonstrate how an analyst might be led astray by incorrectly applying the nonproportionality test.

scholarly journals Model Selection Strategy for Cox Proportional Hazards Model

2019 ◽  
Vol 67 (2) ◽  
pp. 111-116
Author(s):  
Fabiha Binte Farooq ◽  
Md Jamil Hasan Karami
Keyword(s):  
Model Selection ◽  
Proportional Hazards ◽  
Proportional Hazards Model ◽  
Cox Model ◽  
Information Criterion ◽  
Cox Proportional Hazards ◽  
Cox Proportional Hazards Model ◽  
Outcome Variable ◽  
Selection Strategy ◽  
True Model

Often in survival regression modelling, not all predictors are relevant to the outcome variable. Discarding such irrelevant variables is very crucial in model selection. In this research, under Cox Proportional Hazards (PH) model we study different model selection criteria including Stepwise selection, Least Absolute Shrinkage and Selection Operator (LASSO), Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC) and the extended versions of AIC and BIC to the Cox model. The simulation study shows that varying censoring proportions and correlation coefficients among the covariates have great impact on the performances of the criteria to identify a true model. In the presence of high correlation among the covariates, the success rate for identifying the true model is higher for LASSO compared to other criteria. The extended version of BIC always shows better result than the traditional BIC. We have also applied these techniques to real world data. Dhaka Univ. J. Sci. 67(2): 111-116, 2019 (July)

The effect of pathologic T-stage and Gleason score on cancer-specific survival in patients with positive surgical margins after surgery for high-risk prostate cancer.

2014 ◽  
Vol 32 (4_suppl) ◽  
pp. 140-140
Author(s):  
Lorenzo Tosco ◽  
Hendrik Van Poppel ◽  
Thomas Van den Broeck ◽  
Patrick Bastian ◽  
Alberto Briganti ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
High Risk ◽  
Gleason Score ◽  
Proportional Hazards Model ◽  
Cox Model ◽  
Cox Proportional Hazards Model ◽  
Cancer Specific Survival ◽  
High Risk Prostate Cancer ◽  
Kaplan Meier ◽  
Risk Prostate Cancer

140 Background: High-risk prostate cancer (HRPC) is a challenging disease and the role of surgery is often considered in the context of a multimodal approach but patients with positive section margins (R1) disease have not always the same cancer-specific survival (CSS). The current study aims to analyze current postoperative pathological features in order to predict CSS of HRPC patients with R1, but with negative lymph nodes (pN0), treated with surgery. Methods: From a multi-institutional retrospective cohort of 5,876 HRPC patients treated by radical prostatectomy and pelvic lymph node dissection, 1541 patients with pN0 and R1 were selected. Following surgery, adjuvant EBRT and/or ADT were delivered according to institutional protocols. Patients were subdivided into four groups according to pT stage (pT≥3 and pT<3) and p-Gleason score (pGS≥8 and pGS<8). Kaplan-Meier plots with log-rank tests and a Cox proportional hazards model were applied to study CSS. All significance levels were set at 0.05. MedCalc was used for all statistical analyses. Results: Median age at surgery was 66 years (42-89). Of all patients, 399 (25.9%) had GS≥8 and 999 (64.8%) had pT≥3 at definitive histopathology. Patients were classified as COMBO groups: C1 (423; 27.4%; GS<8,pT<3), C2 (674; 43.7%; GS<8, pT≥3), C3 (83; 5.4%; GS≥8, pT<3), C4 (362; 23.5%; GS≥8, pT≥3). Adjuvant EBRT and ADT, respectively, were delivered in C1 3%/5%, C2 15%/21%, C3 21%/20%, C4 28%/40%. Kaplan-Meier plots demonstrated statistically different 10-yr CSS between groups: C1 97%, C2 93.8%, C3 85.1% and C4 77.3% (p<0.0001). COMBO groups were also compared using a Cox model and results are shown in the Table. Conclusions: COMBO groups demonstrated to be able to subdivide margin-positive, pN0 HRPC into 4 demarcated groups with significantly different CSS. This subdivision could be considered an easy-to-use tool which can help for counseling patients for adjuvant treatment strategies. [Table: see text]

Influence of prior and subsequent pregnancy on breast cancer prognosis.

1995 ◽  
Vol 13 (2) ◽  
pp. 430-434 ◽  
Author(s):  
E von Schoultz ◽  
H Johansson ◽  
N Wilking ◽  
L E Rutqvist
Keyword(s):  
Breast Cancer ◽  
Cancer Diagnosis ◽  
Proportional Hazards Model ◽  
Cox Model ◽  
Breast Cancer Diagnosis ◽  
Subsequent Pregnancy ◽  
Cox Proportional Hazards Model ◽  
Cancer Prognosis ◽  
Control Group ◽  
Worse Prognosis

PURPOSE AND METHODS The prognostic influence of pregnancies 5 years before (n = 173) and after (n = 50) breast cancer diagnosis was investigated in 2,119 women less than 50 years of age with a primary operable breast cancer. The main end point was distant metastasis. Univariate and multivariate analyses were performed using the Cox proportional hazards model. In the analyses of the effect of pregnancy after diagnosis of breast cancer, a Cox model with a time-dependent covariate was applied. RESULTS Women with a pregnancy before diagnosis had slightly larger tumors than the control group. However, they did not differ with respect to nodal status and estrogen receptor (ER) status. There was no evidence that women with a pregnancy during the 5-year period preceding breast cancer diagnosis had a worse prognosis compared with women without pregnancy during the same period. Similarly, there was no evidence that women with a pregnancy after breast cancer diagnosis had a worse prognosis. CONCLUSION The hormonal changes associated with pregnancy thus seem to have little, if any, influence on the prognosis of breast cancer. In the present study, at least, there was no indication of a worse prognosis. In fact, the relative hazard for women who became pregnant after diagnosis of breast cancer in comparison with women without a subsequent pregnancy was 0.48 (P = .14), which suggested a possible decreased risk of distant dissemination.

scholarly journals Combining Symbolic Regression with the Cox Proportional Hazards Model Improves Prediction of Heart Failure Deaths

2021 ◽  
Author(s):  
Casper Wilstrup ◽  
Chris Cave
Keyword(s):  
Heart Failure ◽  
Medical Records ◽  
Proportional Hazards ◽  
Proportional Hazards Model ◽  
Cox Model ◽  
Symbolic Regression ◽  
Cox Proportional Hazards ◽  
Cox Proportional Hazards Model ◽  
Data Set ◽  
Hazards Model

Abstract Background: Heart failure is a clinical syndrome characterised by a reduced ability of the heart to pump blood. Patients with heart failure have a high mortality rate, and physicians need reliable prognostic predictions to make informed decisions about the appropriate application of devices, transplantation, medications, and palliative care. In this study, we demonstrate that combining symbolic regression with the Cox proportional hazards model improves the ability to predict death due to heart failure compared to using the Cox proportional hazards model alone. Methods: We used a newly invented symbolic regression method called the QLattice to analyse a data set of medical records for 299 Pakistani patients diagnosed with heart failure. The QLattice identified a minimal set of mathematical transformations of the available covariates, which we then used in a Cox model to predict survival.Results: An exponential function of age, the inverse of ejection fraction, and the inverse of serum creatinine were identified as the best risk factors for predicting heart failure deaths. A Cox model fitted on these transformed covariates had improved predictive performance compared with a Cox model on the same covariates without mathematical transformations. Conclusion: Symbolic regression is a way to find transformations of covariates from patients’ medical records which can improve the performance of survival regression models. At the same time, these simple functions are intuitive and easy to apply in clinical settings. The direct interpretability of the simple forms may help researchers gain new insights into the actual causal pathways leading to deaths.

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