Risk as a pattern over time: Delineation of time-dependent risk factors in biological,psychological, and social variables in cancer patients

Background

Survival in cancer patients is associated with a multitude of biological, social, and psychological factors. Although it is well established that all these factors add to overall mortality, it is not well understood how the predictive power of these parameters changes in a comprehensive model and over time.

Methods

Patients who attended the authors’ outpatient clinic were invited to participate. The authors followed 5180 mixed cancer patients (51.1% female; mean age, 59.1 years [SD = 13.8]) for up to 16 years and analyzed biological (age, sex, cancer site, anemia), psychological (anxiety, depression), and social variables (marital status, education, employment status) potentially predicting overall survival in a Cox proportional hazards model.

Results

The median survival time for the entire sample was 4.3 years (95% confidence interval, 4.0–4.7). The overall survival probabilities for 1 and 10 years were 76.8% and 38.0%, respectively. Following an empirical approach, the authors split the time interval into five periods: acute, subacute, short-term, medium-term, and long-term. A complex pattern of variables predicted overall survival differently in the five periods. Biological parameters were important throughout most of the time, social parameters were either time-independent predictors or tended to be more important in the longer term. Of the psychological parameters, only depression was a significant predictor and lost its predictive power in the long-term.

Conclusions

The findings of this study allow the development of comprehensive patient-specific models of risk and resilience factors addressing biopsychosocial needs of cancer patients, paving the way for a personalized treatment plan that goes beyond biomedical cancer care.     

集束化干预联合闭环管理对ICU多重耐药菌感染的防控效果

目的 探讨集束化干预策略联合闭环管理模式对ICU多重耐药菌感染的防控效果。方法选取2020年1~12月EICU住院患者275例作为对照组，实施常规管理；2021年1~10月EICU住院患者239例作为观察组，在常规管理基础上实施集束化干预策略联合闭环管理。结果实施后，观察组多重耐药菌感染发生率明显低于对照组；患者住院日显著低于对照组，4项护理措施执行率(隔离标识、手卫生、环境消毒、医务人员相关知识知晓)、病原学送检率显著高于对照组（均P<0.05）。结论集束化干预联合信息化闭环管理可有效降低EICU多重耐药菌感染发生率。     

Contribution of cost to treatment nonadherence in the US breast cancer survivors: a population-based analysis

Breast Cancer Research and Treatment - Breast cancer survivors are often prescribed medications for at least 5&nbsp;years to reduce recurrence risk, yet some forego this treatment due to cost....     

Comparative evaluation of serological tests used for the diagnosis of rickettsial diseases prevalent in the temperate region of North India

PurposeThe clinical manifestations of rickettsial diseases mimic other endemic infections with similar presentations thus posing a serious challenge to clinicians for their diagnosis. For the diagnosis of rickettsial disease serological tests like Weil Felix, ELISA and IFA are used. There are limited studies that have evaluated different serological tests for the diagnosis of rickettsial diseases. Therefore, the present study was undertaken to evaluate the ELISA and Weil Felix test for the diagnosis of rickettsial diseases prevalent in this region.MethodsSamples from 281 patients clinically suspected of rickettsial diseases were tested for spotted fever group (SFG), typhus group (TG) and scrub typhus group (STG) by Weil Felix, ELISA and IFA was taken as the gold standard. Baseline titers and cut-off ODs were calculated by taking samples from healthy blood donors.ResultsThe sensitivity, specificity, positive and negative predictive value of Weil Felix test ranged from 30% to 44%, 83.46%–97.86%, 9%–77%, 92–96% respectively. The sensitivity and specificity, positive and negative predictive value of ELISA ranged from 80.77% to 96.15%, 96.33%–98.43%, 70.21%–88.64%, 92.89%–99.60% respectively. Maximum cross-reactions were observed between SFG and STG by the Weil Felix test and between STG and TG by ELISA.ConclusionsELISA was found to be sensitive and specific for the diagnosis of rickettsial diseases. It is easy to perform, does not require a technical expert for result interpretation and a large number of samples can be processed at a time.