Genetic progression and the waiting time to cancer.
Ontology highlight
ABSTRACT:
SUBMITTER: Beerenwinkel N
PROVIDER: S-EPMC2065895 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Emerging diseases must make a transition from stuttering chains of transmission to sustained chains of transmission, but this critical transition need not coincide with the system becoming supercritical. That is, the introduction of infection to a supercritical system results in a significant fraction of the population becoming infected only with a certain probability. Understanding the waiting time to the first major outbreak of an emerging disease is then more complicated than determining when the system becomes supercritical. We treat emergence as a dynamic bifurcation, and use the concept of bifurcation delay to understand the time to emergence after a system becomes supercritical. Specifically, we consider an SIR model with a time-varying transmission term and random infections originating from outside the population. We derive an analytic density function for the delay times and find it to be, in general, in agreement with stochastic simulations. We find the key parameters to be the rate of introduction of infection and the rate of change of the basic reproductive ratio. These findings aid our understanding of real emergence events, and can be incorporated into early-warning systems aimed at forecasting disease risk.
| S-EPMC5095216 | biostudies-literature
Project description:Wait time and scheduling for outpatient chemotherapy administration depends on various factors including infusion room hours of operation, availability of oncologists, nursing and pharmacy staffing, and physical space limitations. The aim of this study was to use the electronic event log of patients on health information system (HIS) to map and analyze patient flow in advanced metastatic colorectal patients at an academic cancer center. From January 2009 to December 2014, patients who were diagnosed with metastatic colorectal cancer and received outpatient chemotherapy confined to FOLFIRI (fluorouracil, leucovorin, and irinotecan) or FOLFOX (folinic acid, fluorouracil, and oxaliplatin) were identified. From the HIS, patient flow was mapped by collection of event records including blood collection and pretreatment laboratory test, arrival to outpatient clinics, outpatient session (interview, drug accountability and appointment scheduling), and initiation of chemotherapy. A total of 10,638 patients were analyzed for 136,281 outpatient visits. The total office stay time from outpatient registration to initiation of chemotherapy was 92.58 ± 87.96 (mean ± standard deviation) minutes. Each outpatient session lasted 23.75 ± 51.55 minutes. After completing the outpatient session, patients waited 1,657.23 ± 3,027.65 minutes before chemotherapy and 46.66 ± 75.94 minutes within infusion room. Compared to the prior first come first serve rule, the new reservation system showed an improvement in overall waiting time from 2,432.3 ± 4,822.9 to 2,386.7 ± 143.4 minutes; however, waiting time within infusion room slightly increased from 36.68 ± 49.33 to 48.13 ± 46.32 minutes. Our findings indicate that transaction data analytics from HIS can be used to evaluate patient flow within oncology outpatient practice based on real-world hospital data.
| S-EPMC7523863 | biostudies-literature
Project description:BackgroundLong waiting times result in low satisfaction. Although several methods are used to shorten the actual waiting time (AWT) in large hospitals of China, the outpatients still have a long actual waiting time. This study aimed to explore whether satisfaction could be improved by extending the expected waiting time (EWT) instead of shortening the AWT.MethodsIn October 2016, 257 students in grade one voluntarily participated in this study. They came from 6 classes, which were randomly divided into two groups: 3 classes comprised the control group (n?=?125) and 3 classes comprised the experimental group (n?=?132). Unfavorable information (UI) was given to the experimental group alone. Six distinct questionnaires were designed to explore the effects of UI on EWT and the effects of an extended EWT on satisfaction. Satisfaction scores ranged from 0 to 100: 0-25, very dissatisfied; 26-50, dissatisfied; 51-75, satisfied; 76-100, very satisfied. Each participant finished one of the 6 questionnaires online. Of the 257 questionnaires, 233 were valid.ResultsBefore UI was given, the initial EWT (T0) was similar between the control and experimental groups (Z?=?-1.924, P?=?0.054). Under the effects of UI, individuals in the experimental group extended their EWT (T1) from 121.0 to 180.0?min (Z?=?-6.367, P?<?0.001). Females prolonged their EWT longer than males did (Z?=?-2.239, P?=?0.025). Then, this study defined T0?=?1.5?h and T1?=?2.5?h, and compared the satisfaction scores between the control and experimental groups: a significant difference was found when AWT =2.0?h (t?=?-?3.568, P?=?0.001), but not when AWT =3.0?h (t?=?-?0.718, P?=?0.475) or when AWT =1.0?h (t?=?-?1.088, P?=?0.280). When AWT =3.0?h, fewer individuals felt "very dissatisfied" in the experimental group (21.2%) than in the control group (44.7%) (?2?=?4.368, P?=?0.037).ConclusionsEWT was found to be extended greatly by UI. An extended EWT could improve satisfaction scores.
| S-EPMC6693195 | biostudies-literature
Project description:Background and objectivesApproximately 20% of deceased donor kidneys are discarded each year in the United States. Some of these kidneys could benefit patients who are waitlisted. Understanding patient preferences regarding accepting marginal-quality kidneys could help more of the currently discarded kidneys be transplanted.Design, setting, participants, & measurementsThis study uses a discrete choice experiment that presents a deceased donor kidney to patients who are waiting for, or have received, a kidney transplant. The choices involve trade-offs between accepting a kidney today or a future kidney. The options were designed experimentally to quantify the relative importance of kidney quality (expected graft survival and level of kidney function) and waiting time. Choices were analyzed using a random-parameters logit model and latent-class analysis.ResultsIn total, 605 participants completed the discrete choice experiment. Respondents made trade-offs between kidney quality and waiting time. The average respondent would accept a kidney today, with 6.5 years of expected graft survival (95% confidence interval, 5.9 to 7.0), to avoid waiting 2 additional years for a kidney, with 11 years of expected graft survival. Three patient-preference classes were identified. Class 1 was averse to additional waiting time, but still responsive to improvements in kidney quality. Class 2 was less willing to accept increases in waiting time for improvements in kidney quality. Class 3 was willing to accept increases in waiting time even for small improvements in kidney quality. Relative to class 1, respondents in class 3 were likely to be age ≤61 years and to be waitlisted before starting dialysis, and respondents in class 2 were more likely to be older, Black, not have a college degree, and have lower Karnofsky performance status.ConclusionsParticipants preferred accepting a lower-quality kidney in return for shorter waiting time, particularly those who were older and had lower functional status.
| S-EPMC9625104 | biostudies-literature
Project description:BACKGROUND:Cancer Waiting Time targets have been integrated into successive cancer strategies as indicators of cancer care quality in England. These targets are reported in national statistics for all cancers combined, but there is mixed evidence of their benefits and it is unclear if meeting Cancer Waiting Time targets, as currently defined and published, is associated with improved survival for individual patients, and thus if survival is a good metric for judging the utility of the targets. METHODS AND FINDINGS:We used individually-linked data from the National Cancer Waiting Times Monitoring Dataset (CWT), the cancer registry and other routinely collected datasets. The study population consisted of all adult patients diagnosed in England (2009-2013) with colorectal (164,890), lung (171,208) or ovarian (24,545) cancer, of whom 82%, 76%, and 77%, respectively, had a CWT matching record. The main outcome was one-year net survival for all matched patients by target attainment ('met/not met'). The time to each type of treatment for the 31-day and 62-day targets was estimated using multivariable analyses, adjusting for age, sex, tumour stage and deprivation. The two-week wait (TWW) from GP referral to specialist consultation and 31-day target from decision to treat to start of treatment were met for more than 95% of patients, but the 62-day target from GP referral to start of treatment was missed more often. There was little evidence of an association between meeting the TWW target and one-year net survival, but for the 31-day and 62-day targets, survival was worse for those for whom the targets were met (e.g. colorectal cancer: survival 89.1% (95%CI 88.9-89.4) for patients with 31-day target met, 96.9% (95%CI 96.1-91.7) for patients for whom it was not met). Time-to-treatment analyses showed that treatments recorded as palliative were given earlier in time, than treatments with potentially curative intent. There are possible limitations in the accuracy of the categorisation of treatment variables which do not allow for fully distinguishing, for example, between curative and palliative intent; and it is difficult in these data to assess the appropriateness of treatment by stage. These limitations in the nature of the data do not affect the survival estimates found, but do mean that it is not possible to separate those patients for whom the times between referral, decision to treat and start of treatment could actually have an impact on the clinical outcomes. This means that the use of these survival measures to evaluate the targets would be misleading. CONCLUSIONS:Based on these individually-linked data, and for the cancers we looked at, we did not find that Cancer Waiting Time targets being met translates into improved one-year survival. Patients may benefit psychologically from limited waits which encourage timely treatment, but one-year survival is not a useful measure for evaluating Trust performance with regards to Cancer Waiting Time targets, which are not currently stratified by stage or treatment type. As such, the current composition of the data means target compliance needs further evaluation before being used for the assessment of clinical outcomes.
| S-EPMC6104918 | biostudies-literature
Project description:BACKGROUND:In early diagnosis studies on symptomatic cancer, survival was the most recommended outcome. The magnitude and impact of the patient interval and primary care interval is well-known in oral cancer; however, the hospital interval and its influence on surviving this neoplasia are not well known. AIMS:To quantify the interval between the first contact with the specialist and the start of treatment for patients with oral cancer and to evaluate whether there was a link between this interval and disease survival. METHODS:We designed a hospital-based study that included 228 patients diagnosed with oral/oropharyngeal squamous cell carcinoma between 1998 and 2008 at A Coruña University Hospital (Spain) who were followed up until 2016. The data were extracted retrospectively from hospital medical charts. The study interval was defined in the context of the "pathways to treatment" model as the interval from the first specialist visit (start point) to the start of treatment (end point). We calculated the total interval (from first symptom to treatment) to evaluate the relative length of the hospital interval, and we considered the variables age, sex, location, comorbidity and tumour classification stage. Survival time was defined as the interval from the first treatment to death or censoring. RESULTS:The median hospital interval was 20 days, with an interquartile range of 15-29.1 days. The most relevant prognostic variable was the tumour stage (III-IV: Exp. ß = 2.8, p = 0.001). The hospital interval was part of the multivariate model, and its association with mortality showed a V-shaped association, where patients with short hospital intervals (3-18 days) and those with long hospital intervals (26-55 days) had significantly higher mortality than those with medium hospital intervals (19-25 days). CONCLUSION:The hospital interval represents a relevant interval for the patient's path towards treatment, has prognostic implications and is subject to a severity bias (waiting time paradox) that should be avoided.
| S-EPMC6814211 | biostudies-literature
Project description:To gain a better understanding of the evolutionary dynamics of regulatory DNA sequences, we address the following questions: (1) How long does it take until a given transcription factor (TF) binding site emerges at random in a promoter sequence? and (2) How does the composition of a TF binding site affect this waiting time? Using two different probabilistic models (an i.i.d. model and a neighbor dependent model), we can compute the expected waiting time for every k-mer, k ranging from 5 to 10, until it appears in a promoter of a species. Our findings indicate that new TF binding sites can be created on a short evolutionary time scale, i.e. in a time span below the speciation time of human and chimp. Furthermore, one can conclude that the composition of a TF binding site plays a crucial role concerning the waiting time until it appears and that the CpG methylation-deamination substitution process probably accelerates the creation of new TF binding sites. A screening of existing TF binding sites moreover reveals that k-mers predicted to have short waiting times occur more frequently than others. Supplementary Material is available at www.libertonline.com/cmb .
| S-EPMC3119604 | biostudies-literature
Project description:BackgroundThe time delay between the start of an influenza pandemic and its subsequent initiation in other countries is highly relevant to preparedness planning. We quantify the distribution of this random time in terms of the separate components of this delay, and assess how the delay may be extended by non-pharmaceutical interventions.Methods and findingsThe model constructed for this time delay accounts for: (i) epidemic growth in the source region, (ii) the delay until an infected individual from the source region seeks to travel to an at-risk country, (iii) the chance that infected travelers are detected by screening at exit and entry borders, (iv) the possibility of in-flight transmission, (v) the chance that an infected arrival might not initiate an epidemic, and (vi) the delay until infection in the at-risk country gathers momentum. Efforts that reduce the disease reproduction number in the source region below two and severe travel restrictions are most effective for delaying a local epidemic, and under favourable circumstances, could add several months to the delay. On the other hand, the model predicts that border screening for symptomatic infection, wearing a protective mask during travel, promoting early presentation of cases arising among arriving passengers and moderate reduction in travel volumes increase the delay only by a matter of days or weeks. Elevated in-flight transmission reduces the delay only minimally.ConclusionsThe delay until an epidemic of pandemic strain influenza is imported into an at-risk country is largely determined by the course of the epidemic in the source region and the number of travelers attempting to enter the at-risk country, and is little affected by non-pharmaceutical interventions targeting these travelers. Short of preventing international travel altogether, eradicating a nascent pandemic in the source region appears to be the only reliable method of preventing country-to-country spread of a pandemic strain of influenza.
| S-EPMC1764036 | biostudies-literature
Project description:PurposeThis study aimed to evaluate the effect of waiting time, from diagnosis to initiation of definitive concurrent chemoradiation (CCRT), on overall survival in cervical cancer patients.Materials and methodsPatients with cervical cancer who were treated with definitive CCRT between 2000 and 2017 were retrospectively reviewed. Time from initial pathological diagnosis to definitive CCRT was analyzed both as a continuous variable (per day) and as a categorical variable in two groups (group 1 ≤ median, group 2 > median). Patients with a waiting time of more than 60 days were excluded.ResultsThe median waiting time was 14 days (0-60). There were differences between group 1 and group 2 in age and chemotherapy regimens. However, no significant difference was found in the International Federation of Gynecology and Obstetrics stage, cell type, or the number of cycles of chemotherapy received during CCRT. A longer waiting time was associated with poorer overall survival on the Kaplan-Meier curve (group 1 vs. group 2, p=0.042). On multivariate analysis, intervals as either a continuous variable (hazard ratio [HR], 1.023; 95% confidence interval [CI], 1.006 to 1.040; p=0.007) or a categorical variable (HR, 1.513; 95% CI, 1.073 to 2.134; p=0.018), FIGO stage, cell type, and the number of cycles of chemotherapy received during CCRT were significant independent prognostic factors for overall survival.ConclusionA shorter waiting time from pathological diagnosis to definitive CCRT showed benefit on overall survival. Our findings suggest that an effort to minimize waiting times should be recommended in cervical cancer patients who are candidates for CCRT.
| S-EPMC8756115 | biostudies-literature
Project description:We aim to perform a register-based, observational cohort study to investigate whether it is safe to delay start of treatment with a curative intention after a colorectal cancer diagnosis. Our hypothesis is that delaying start of treatment until 4-8 weeks is as safe as starting treatment within four weeks after a colorectal cancer diagnosis.
| 2353262 | ecrin-mdr-crc