Class | Hazard ratio | Parameter estimate | prob chi sq |
---|---|---|---|
OD | 1.234 | 0.21002 | <0.0001 |
Frederick K Ho, research associate,1 Stuart R Gray, senior lecturer,2 Paul Welsh, senior lecturer,2 Fanny Petermann-Rocha, PhD student,1,2 Hamish Foster, clinical academic GP fellow,1 Heather Waddell, PhD student,2 Jana Anderson, research fellow,1 Donald Lyall, lecturer,1 Naveed Sattar, professor,2 Jason M R Gill, professor,2 John C Mathers, professor,3 Jill P Pell, professor,1 and Carlos Celis-Morales, research fellow1,2,4,5 submitted by Ricosss to ketoscience [link] [comments] https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7190059/ AbstractObjectiveTo investigate the association of macronutrient intake with all cause mortality and cardiovascular disease (CVD), and the implications for dietary advice.DesignProspective population based study.SettingUK Biobank.Participants195 658 of the 502 536 participants in UK Biobank completed at least one dietary questionnaire and were included in the analyses. Diet was assessed using Oxford WebQ, a web based 24 hour recall questionnaire, and nutrient intakes were estimated using standard methodology. Cox proportional models with penalised cubic splines were used to study non-linear associations.Main outcome measuresAll cause mortality and incidence of CVD.Results4780 (2.4%) participants died over a mean 10.6 (range 9.4-13.9) years of follow-up, and 948 (0.5%) and 9776 (5.0%) experienced fatal and non-fatal CVD events, respectively, over a mean 9.7 (range 8.5-13.0) years of follow-up. Non-linear associations were found for many macronutrients. Carbohydrate intake showed a non-linear association with mortality; no association at 20-50% of total energy intake but a positive association at 50-70% of energy intake (3.14 v 2.75 per 1000 person years, average hazard ratio 1.14, 95% confidence interval 1.03 to 1.28 (60-70% v 50% of energy)). A similar pattern was observed for sugar but not for starch or fibre. A higher intake of monounsaturated fat (2.94 v 3.50 per 1000 person years, average hazard ratio 0.58, 0.51 to 0.66 (20-25% v 5% of energy)) and lower intake of polyunsaturated fat (2.66 v 3.04 per 1000 person years, 0.78, 0.75 to 0.81 (5-7% v 12% of energy)) and saturated fat (2.66 v 3.59 per 1000 person years, 0.67, 0.62 to 0.73 (5-10% v 20% of energy)) were associated with a lower risk of mortality. A dietary risk matrix was developed to illustrate how dietary advice can be given based on current intake.ConclusionMany associations between macronutrient intake and health outcomes are non-linear. Thus dietary advice could be tailored to current intake. Dietary guidelines on macronutrients (eg, carbohydrate) should also take account of differential associations of its components (eg, sugar and starch). https://preview.redd.it/wgngpelueij51.png?width=1002&format=png&auto=webp&s=5ee8efcb1b02b036dfbe180fca4abc82f4da77d4 https://preview.redd.it/iz1wgw65fij51.png?width=1002&format=png&auto=webp&s=4ec39eddcc7d0bf9bddd7264b61a1a4b1d9678c7 Strengths and limitations of this studyA strength of this study is that we did not assume linearity between intakes of macronutrients and health outcomes and we adjusted mutually for macronutrient components. We also explored associations with constituent components of macronutrients—for example, starch, sugar, and dietary fibre are components of carbohydrates, each of which has distinctive relations with health outcomes. The possibility of confounding was dealt with through statistical adjustment for a wide range of covariates and through a series of sensitivity analyses. As with any observational study, however, residual confounding is possible, and causation cannot be tested. Also, summary statistics and estimates of absolute risk from this study might not be generalisable even though the personal characteristics of the cohort and estimated effect sizes are similar to those of the general population.36 37 38 As the dietary information used in this study was provided by around half of UK Biobank participants, selection bias is possible. Dietary measurements in our study were derived from 24 hour recall so might not portray participants’ typical intake precisely and could be subject to recall bias.39 Owing to limited statistical power, we did not exclude participants who did not provide multiple dietary records, and some analyses might be underpowered. Further, we were not able to reliably test whether some associations were sex specific. Similarly, associations at the extreme ends of intake (particularly intakes with wide confidence intervals) should be interpreted with caution. Isocaloric replacement analysis is based on comparisons between participants and might not represent real life changes as occurs in randomised controlled trials. We were unable to investigate associations with added sugars, trans fat, types of polyunsaturated fat (omega-3 and omega-6), and animal based versus plant based protein because these data were not available. Also, food source (eg, whole grain versus refined carbohydrate sources) might modify the associations between macronutrient intake and outcomes. The dietary risk matrix was constructed for illustrative purposes rather than as a tool ready for implementation, and the cut-off values have not been validated. |
Abstract
Background: Patients are at increased risk of cardiovascular complications while recovering from sepsis. We aimed to study the temporal change and susceptible periods for cardiovascular complications in patients recovering from sepsis by using a national database.
Methods: In this retrospective population-based cohort study, patients with sepsis were identified from the National Health Insurance Research Database in Taiwan. We estimated the risk of myocardial infarction (MI) and stroke following sepsis by comparing a sepsis cohort to a matched population and hospital control cohort. The primary outcome was first occurrence of MI or stroke requiring admission to hospital during the 180-day period following discharge from hospital after sepsis. To delineate the risk profile over time, we plotted the weekly risk of MI and stroke against time using the Cox proportional hazards model. We determined the susceptible period by fitting the 2 phases of time-dependent risk curves with free-knot splines, which highlights the turning point of the risk of MI and stroke after discharge from the hospital.
Results: We included 42 316 patients with sepsis; stroke developed in 831 of these patients and MI developed in 184 within 180 days of discharge from hospital. Compared with population controls, patients recovering from sepsis had the highest risk for MI or stroke in the first week after discharge (hazard ratio [HR] 4.78, 95% confidence interval [CI] 3.19 to 7.17; risk difference 0.0028, 95% CI 0.0021 to 0.0034), with the risk decreasing rapidly until the 28th day (HR 2.38, 95% CI 1.94 to 2.92; risk difference 0.0045, 95% CI 0.0035 to 0.0056) when the risk stabilized. In a repeated analysis comparing the sepsis cohort with the nonsepsis hospital control cohort, we found an attenuated but still marked elevated risk before day 36 after discharge (HR 1.32, 95% CI 1.15 to 1.52; risk difference 0.0026, 95% CI 0.0013 to 0.0039). The risk of MI or stroke was found to interact with age, with younger patients being associated with a higher risk than older patients (interaction p = 0.0004).
Interpretation: Compared with the general population with similar characteristics, patients recovering from sepsis had a markedly elevated risk of MI or stroke in the first 4 weeks after discharge from hospital. More close monitoring and pharmacologic prevention may be required for these patients at the specified time.
fit <- coxph( Surv(tstart, tstop, had_event) ~ review_event, data = newdatatestcum) summary(fit)The output:
n= 35695, number of events= 54 coef exp(coef) se(coef) z Pr(>|z|) review_event -0.006707 0.993316 0.001771 -3.786 0.000153 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 exp(coef) exp(-coef) lower .95 upper .95 review_event 0.9933 1.007 0.9899 0.9968 Concordance= 0.693 (se = 0.041 ) Rsquare= 0.001 (max possible= 0.014 ) Likelihood ratio test= 24.1 on 1 df, p=9.123e-07 Wald test = 14.34 on 1 df, p=0.0001529 Score (logrank) test = 11.58 on 1 df, p=0.0006658Along with this I have used the cox.zph function to test the proportional hazard and from my study so far I do not believe to have violated.
cox.zph(fit)The ouput:
rho chisq p review_event -0.0699 0.332 0.564The plot:
plot(cox.zph(fit,transform = "log"))(http://i.imgur.com/nf8vwIi.jpg)
Open Access Peer-reviewed Research Article Affiliation: Department of Clinical Neuroscience, Division of Psychiatry, Karolinska Institutet, Stockholm, Sweden Affiliation: Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden Affiliation: Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden Affiliation: Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden Affiliations: Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden, Centre for Violence Prevention, Karolinska Institutet, Stockholm, Sweden * E-mail: [email protected] Affiliations: Department of Clinical Neuroscience, Division of Psychiatry, Karolinska Institutet, Stockholm, Sweden, Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden, Institute of Neuroscience and Physiology, The Sahlgrenska Academy at Gothenburg University, Gothenburg, Sweden The treatment for transsexualism is sex reassignment, including hormonal treatment and surgery aimed at making the person's body as congruent with the opposite sex as possible. Hazard ratios (HR) with 95% confidence intervals (CI) for mortality and psychiatric morbidity were obtained with Cox regression models, which were adjusted for immigrant status and psychiatric morbidity prior to sex reassignment (adjusted HR [aHR]).Our findings suggest that sex reassignment, although alleviating gender dysphoria, may not suffice as treatment for transsexualism, and should inspire improved psychiatric and somatic care after sex reassignment for this patient group.Funding: Financial support was provided through the regional agreement on medical training and clinical research (ALF) between Stockholm County Council and the Karolinska Institutet, and through grants from the Swedish Medical Research Council (K2008-62x-14647-06-3) and the Royal Swedish Academy of Sciences (Torsten Amundson's Foundation).The treatment for transsexualism includes removal of body hair, vocal training, and cross-sex hormonal treatment aimed at making the person's body as congruent with the opposite sex as possible to alleviate the gender dysphoria.With respect to suicide and deaths from other causes after sex reassignment, an early Swedish study followed 24 transsexual persons for an average of six years and reported one suicide.[9] A large Dutch single-centre study (N = 1,109), focusing on adverse events following hormonal treatment, compared the outcome after cross-sex hormone treatment with national Dutch standardized mortality and morbidity rates and found no increased mortality, with the exception of death from suicide and AIDS in male-to-females 25–39 years of age.[9] , [18] A recent systematic review and meta-analysis concluded that approximately 80% reported subjective improvement in terms of gender dysphoria, quality of life, and psychological symptoms, but also that there are studies reporting high psychiatric morbidity and suicide rates after sex reassignment.[6] , [9] , [12] , [21] , [24] , [28] , [29] , [30] Forth, several follow-up studies are hampered by limited follow-up periods. Here, we assessed mortality, psychiatric morbidity, and psychosocial integration expressed in criminal behaviour after sex reassignment in transsexual persons, in a total population cohort study with long-term follow-up information obtained from Swedish registers.National censuses based on mandatory self-report questionnaires completed by all adult citizens in 1960, 1970, 1980, and 1990 provided information on individuals, households, and dwellings, including gender, living area, and highest educational level.In Sweden, a person presenting with gender dysphoria is referred to one of six specialised gender teams that evaluate and treat patients principally according to international consensus guidelines: Standards of Care.A person was defined as exposed to sex reassignment surgery if two criteria were met: (i) at least one inpatient diagnosis of gender identity disorder diagnosis without concomitant psychiatric diagnoses in the Hospital Discharge Register, and (ii) at least one discrepancy between gender variables in the Medical Birth Register (from 1973 and onwards) or the National Censuses from 1960, 1970, 1980, or 1990 and the latest gender designation in the Total Population Register. The date of sex reassignment (start of follow-up) was defined as the first occurrence of a gender identity disorder diagnosis, without any other concomitant psychiatric disorder, in the Hospital Discharge Register after the patient changed sex status (any discordance in sex designation across the Censuses, Medical Birth, and Total Population registers). Using these criteria, a total of 804 patients with gender identity disorder were identified, whereof 324 displayed a shift in the gender variable during the period 1973–2003.Gender identity disorder was coded according to ICD-8: 302.3 (transsexualism) and 302.9 (sexual deviation NOS); ICD-9: 302 (overall code for sexual deviations and disorders, more specific codes were not available in ICD-9); and ICD-10: F64.0 (transsexualism), F64.1 (dual-role transvestism), F64.8 (other gender identity disorder), and F64.9 (gender identity disorder NOS).To study possible gender-specific effects on outcomes of interest, we used two different control groups: one with the same sex as the case individual at birth (birth sex matching) and the other with the sex that the case individual had been reassigned to (final sex matching).Causes of death (Cause of Death Registry from 1952 and onwards) were defined according to ICD as suicide (ICD-8 and ICD-9 codes E950-E959 and E980-E989, ICD-10 codes X60-X84 and Y10-Y34); cardiovascular disease (ICD-8 codes 390-458, ICD-9 codes 390-459, ICD-10 codes I00-I99); neoplasms (ICD-8 and ICD-9 codes 140-239, ICD-10 codes C00-D48), any psychiatric disorder (gender identity disorders excluded); (ICD-8 codes 290-301 and 303-315, ICD-9 codes 290-301 and 303-319, ICD-10 codes F00-F63 and F65-F99); alcohol/drug abuse and dependence (ICD-8 codes 303-304, ICD-9 codes 303-305 (tobacco use disorder excluded), ICD-10 codes F10-F16 and F18-F19 (x5 excluded); and accidents (ICD-8 and ICD-9 codes E800-E929, ICD-10 codes V01-X59). Any criminal conviction during follow-up was counted; specifically, violent crime was defined as homicide and attempted homicide, aggravated assault and assault, robbery, threatening behaviour, harassment, arson, or any sexual offense. [32] Severe psychiatric morbidity was defined as inpatient care according to ICD-8 codes 291, 295-301, 303-304, and 307; ICD-9 codes 291-292, 295-298, 300-301, 303-305 (tobacco use disorder excluded), 307.1, 307.5, 308-309, and 311; ICD-10 codes F10-F16, F18-F25, F28-F45, F48, F50, and F60-F62.Gender-separated analyses were performed and a Kaplan-Meier survival plot graphically illustrates the survival of the sex reassigned cohort and matched controls (all-cause mortality) over time.Immigrant status was twice as common among transsexual individuals compared to controls, living in an urban area somewhat more common, and higher education about equally prevalent.Even though the overall mortality was increased across both time periods, it did not reach statistical significance for the period 1989–2003.Mortality due to cardiovascular disease was moderately increased among the sex-reassigned, whereas the numerically increased risk for malignancies was borderline statistically significant.Transsexual individuals were at increased risk of being convicted for any crime or violent crime after sex reassignment ( Table 2 ); this was, however, only significant in the group who underwent sex reassignment before 1989.By contrast, female-to-males had significantly increased risk of suicide attempts only compared to male controls (aHR 6.8; 95% CI 2.1–21.6) but not compared to female controls (aHR 1.9; 95% CI 0.7–4.8).Second, regarding any crime, male-to-females had a significantly increased risk for crime compared to female controls (aHR 6.6; 95% CI 4.1–10.8) but not compared to males (aHR 0.8; 95% CI 0.5–1.2).The most striking result was the high mortality rate in both male-to-females and female-to males, compared to the general population.[7] , [9] , [10] , [11] Previous clinical studies might have been biased since people who regard their sex reassignment as a failure are more likely to be lost to follow-up. [35] Mortality due to cardiovascular disease was significantly increased among sex reassigned individuals, albeit these results should be interpreted with caution due to the low number of events. [6] , [8] , [10] , [11] suggest that transsexualism is a strong risk factor for suicide, also after sex reassignment, and our long-term findings support the need for continued psychiatric follow-up for persons at risk to prevent this.A previous study of all applications for sex reassignment in Sweden up to 1992 found that 9.7% of male-to-female and 6.1% of female-to-male applicants had been prosecuted for a crime.Many previous studies suffer from low outcome ascertainment, [6] , [9] , [21] , [29] whereas this study has captured almost the entire population of sex-reassigned transsexual individuals in Sweden from 1973–2003.For the purpose of evaluating whether sex reassignment is an effective treatment for gender dysphoria, it is reasonable to compare reported gender dysphoria pre and post treatment. [7] , [12] or retrospectively, [5] , [6] , [9] , [22] , [25] , [26] , [29] , [38] and suggest that sex reassignment of transsexual persons improves quality of life and gender dysphoria.It is therefore important to note that the current study is only informative with respect to transsexuals persons health after sex reassignment; no inferences can be drawn as to the effectiveness of sex reassignment as a treatment for transsexualism.Finally, to estimate start of follow-up, we prioritized using the date of a gender identity disorder diagnosis after changed sex status over before changed sex status, in order to avoid overestimating person-years at risk after sex-reassignment.This study found substantially higher rates of overall mortality, death from cardiovascular disease and suicide, suicide attempts, and psychiatric hospitalisations in sex-reassigned transsexual individuals compared to a healthy control population.
We are interested here in its interpretation and the way it should be reported in the literature. Let’s go ahead. Let’s say for example that you have estimated the hazard ratio between the experimental and the control groups using a statistical model (a classic example: a Cox model) and its value, let’s say, is 2.2. deren Interpretation näher zu beschreiben. Hazard-Funktion 5 Der zentrale Begriff zur Interpretation der Ergeb- nisse des Cox-Modells ist die Hazard-Funktion. Während in einer Kohortenstudie mit festem Be-obachtungszeitraum für alle Probanden und bi-närem Endpunkt, z. B. Tod ja/nein, das Zielereig-nis zu einem festen Zeitpunkt bestimmt wird, ist dieses bei Überlebenszeitstudien mit unter ... Der zentrale Begriff zur Interpretation der Ergebnisse des Cox-Modells ist die Hazard-Funktion. Während in einer Kohorten- studie mit festem Beobachtungszeitraum für alle Probanden und binärem Endpunkt, z.B. Tod ja/nein, das Zielereignis zu ei-nem festen Zeitpunkt bestimmt wird, ist dieses bei Überlebens-zeitstudien mit unterschiedlich langen Beobachtungszeiten nicht oder nur mit großem ... Die Hazard Ratio (oder Hazard Rate) entspricht dem Verhältnis der Hazard Raten zweier Gruppen. Die Hazard Ratio (HR) wird häufig bei klinischen Studien verwendet. Sie gibt das Risikoverhältnis zwischen verschiedenen Behandlungsgruppen an. Dabei wird das Risiko einer Behandlungsgruppe zum Risiko einer 2. Gruppe in Relation gesetzt. Als Beispiel: Bei einer klinischen Studie werden die ... Für multivariable Modelle verwendet man die Cox-Regres-sion. Das Hazard Ratio als deskriptives Maß für den Unter-schied von Überlebenszeiten wird erläutert. Schlussfolgerungen: Wenn nicht spezielle Verfahren bei der Analyse von Überlebenszeitdaten eingesetzt oder de-ren Annahmen nicht überprüft werden, können die Ergeb- nisse fehlerhaft sein. Der Leser einer wissenschaftlichen ... Das stetige Cox-Modell wird auch als proportionales Hazard Modell (proportional hazards model) bezeichnet. Cox-Regression. Einführung Das Cox-Modell Die Cox-Regression in Stata Wie heisst eigentlich ::: Schätzprobleme Das Cox-Modell Das Cox-Modell ist de niert als: h i(t) = h 0(t)exp (X k b kX ik(t)) Die Hazardrate ist de niert als das Produkt einer unspezi zierten Baseline -Funktion h 0(t ... The Cox proportional-hazards model (Cox, 1972) is essentially a regression model commonly used statistical in medical research for investigating the association between the survival time of patients and one or more predictor variables. In the previous chapter (survival analysis basics), we described the basic concepts of survival analyses and methods for ... Die Cox-Regression setzt voraus, dass das Hazard Ratio über die Zeit konstant ist (deshalb auch „proportional hazards regression“ genannt). Das ist der Fall, sobald sich das Ereignisrisiko ... The hazard ratio for these two patients [\(\frac{h_k(t)}{h_{k'}(t)} = \frac{h_0(t)e^{\sum\limits_{i=1}^n{\beta x}}}{h_0(t)e^{\sum ... The R summary for the Cox model gives the hazard ratio (HR) for the second group relative to the first group, that is, female versus male. The beta coefficient for sex = -0.53 indicates that females have lower risk of death (lower survival rates) than males, in ... 5) Das Hazard-Ratio ist zeitabhängig. 6) Die log-log-Überlebenskurven sind eher ungeeignet, um auf grafischem Wege zu beurteilen, ob das Hazard-Ratio zeitabhängig ist oder nicht.
[index] [5594] [677] [9411] [4664] [1539] [1520] [177] [2338] [7283] [7903]
This video provides a demonstration of the use of Cox Proportional Hazards (regression) model based on example data provided in Luke & Homan (1998). A copy ... This video wil help students and clinicians understand how to interpret hazard ratios. Survival analysisTitle: Interpreting coefficients in a multiple explanatory variable Cox proportional hazard model: confounding variableHosmer & Lemeshow Cha... Explore how to fit a Cox proportional hazards model using Stata. We also describes how to check the proportional-hazards assumption statistically using -esta... This short video describes how to interpret a survival plot. Please post any comments or questions below, or at our Statistics for Citizen Scientists group: ... Survival analysis 3 - Using SPSS and R commander (survival plug-in) to carry out Cox regression (proportional hazard analysis)To see the others in this serie... Survival Analysis: Cox Regression - SPSSUsing Cox Regression to Model Customer Time to ChurnGülin Zeynep Öztaş This video provides a demonstration of the use of the Cox proportional hazards model using SPSS. The data comes from a demonstration of this model within the... Kaplan Meier curve and hazard ratio tutorial (Kaplan Meier curve and hazard ratio made simple!) ... Confidence Interval Interpretation. 95% Confidence Interval ... Cox Regresyon Analizi SPSS ... A brief conceptual introduction to hazard ratios and survival curves (also known as Kaplan Meier plots). Hopefully this gives you the information you need to...
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