Persistent symptoms and risk factors predicting prolonged time to symptom-free after SARS‑CoV‑2 infection: an analysis of the baseline examination of the German COVIDOM/NAPKON-POP cohort
Yanyan ShiRalf StroblChristian ApfelbacherThomas BahmerRamsia GeislerPeter U. HeuschmannAnna HornHanno HovenThomas KeilMichael KrawczakLilian KristChristina LemhöferWolfgang LiebBettina Lorenz‐DepiereuxRafael MikolajczykFelipe A. MontellanoJens Peter ReeseStefan SchreiberNicole SkoetzStefan StörkJörg Janne VehreschildMartin WitzenrathEva GrillMaria J. G. T. VehreschildJörg Janne VehreschildHiwa DashtiBarbara LaumerichOliver PociuliNikolaus BüchnerSabine AdlerMathias LehmannSelçuk TasciMaximilian JorczykT. KellerMichael SchrothMartin HowerLukas EberweinTim ZimmermannSimon-Dominik HerkenrathMilena MilovanovicRamona PauliJörg SimonEckard HamelmannChristoph StellbrinkJohannes-Josef TebbeSven StieglitzChristoph WyenJan BoschMirko SteinmüllerChristoph AllerleiMarkus BöbelElke Natascha HeinitzAriane RoeckenAndrea Münckle-KrimlyChristiane GuderianIngmar SilberbaurH. SchäferClaudia RaichleChristoph D. SpinnerBernd SchmeckHeidi AltmannNicole ToepfnerWolfgang SchmidtBjörn‐Erik Ole JensenAndreas E. KremerSabine BlaschkeJochen DutzmannMarylyn M. AddoRobert BalsSven BerckerPhil‐Robin TepasseSandra FrankDirk Müller–WielandAnette FriedrichsJan RuppSiri GöpelJens MaschmannChristine DhillonJacob NattermannIngo VoigtWilfred ObstMartin F. SprinzlChristian ScheerAndreas TeufelUlf GüntherMartin WitzenrathThomas KeilThomas ZöllerSein SchmidtMichael HummelLilian KristJulia FrickeMaria RönnefarthDenise TreueLudie KretzlerChantip Dang‐HeinePaul TrillerAndreas JooßJenny SchlesingerNatalja LiseweskiChristina PleyCarmen ScheibenbogenMarius M. HoeperPhilipp A. ReukenMichael von BergweltRainer NothDaniel DrömannMaria J. G. T. VehreschildSiegbert RiegIstván VadászPhilipp KoehlerUta MerleStefan SchreiberPeter U. HeuschmannStefan StörkAnette FriedrichsAstrid PetersmannClaudia EllertGeorg SchmidtJörg Janne VehreschildKatrin MilgerMarie von LilienfeldMartin WitzenrathOliver WitzkePatrick MeybohmPeter U. HeuschmannSabine BlaschkeSandra FrankStefan SchreiberThomas HeinAndrea WittigAndreas SimmAnette FriedrichsAnke Reinacher‐SchickAnna FreyAntonella IannacconeAstrid PetersmannBenjamin MaasoumyBenjamin WaschkiBimba F. HoyerB. van OorschotCarolina van SchaikChristina LemhöferChristina PolidoriChristine KleinDaniel MedenwaldEva C. SchulteEva GrillFelix G. MeinelFolke BrinkmannGhazal ArabiHeike BickeböllerHolger A. LindnerIldikó GágyorJessica C. HasselJürgen DeckertKatrin MilgerKerstin U. LudwigMarcus DörrMarie von Lilienfeld‐ToalMartin MöckelMartin WeiglMatthias NauckMiriam BanasMünevver DemirNicole LindenbergNora HettichNorma JungOliver WitzkeOrlando Guntinas‐LichiusPatrick MeybohmReinhard BernerSabine BlaschkeSamuel KnaußSandra FrankSebastian E. BaumeisterSebastian DolffSelma UgurelSophia StöckleinStefanie JoosWinfred Häuser. Jörg Janne VehreschildMaximilian SchonsSina M. HopffMarkus BrechtelCristina Schmidt-IbanezJohannes SchneiderCarolin E. M. JakobFranziska Voß. Inga BernemannSonja KunzeMaike TauchertThomas IlligGabriele Anton. Cornelia FiesslerMirjam KohlsOlga MiljukovSteffi Jírů-HillmannJens‐Peter ReesePeter ReesePeter U. HeuschmannAnna‐Lena HofmannJulia SchmidtKathrin UngethümAnna HornMichael Krawczak. Thomas BahmerWolfgang LiebDaniel PapeStefan SchreiberAnne HermesIrina LehmannCorina MaetzlerLukas Tittmann. Roberto LorbeerBettina Lorenz‐DepiereuxMonika KrausChristian SchäferJ. SchallerMario SchattschneiderDana StahlHeike ValentinDagmar KreftingMatthias Nauck. Nicole ToepfnerReinhard Berner. Christof von KalleSylvia ThunAlexander BartschkeLiudmila LysyakovaStefanie RudolphJulian Sass. Eike NagelValentina O. PüntmannTammy WolfThourier AzdadFranziska WeisIra KrückemeierSimon BohlenderDeniz DesikL LaghchiouaRalf HeyderSilke Wiedmann
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We aimed to assess symptoms in patients after SARS-CoV-2 infection and to identify factors predicting prolonged time to symptom-free.COVIDOM/NAPKON-POP is a population-based prospective cohort of adults whose first on-site visits were scheduled ≥ 6 months after a positive SARS-CoV-2 PCR test. Retrospective data including self-reported symptoms and time to symptom-free were collected during the survey before a site visit. In the survival analyses, being symptom-free served as the event and time to be symptom-free as the time variable. Data were visualized with Kaplan-Meier curves, differences were tested with log-rank tests. A stratified Cox proportional hazard model was used to estimate adjusted hazard ratios (aHRs) of predictors, with aHR < 1 indicating a longer time to symptom-free.Of 1175 symptomatic participants included in the present analysis, 636 (54.1%) reported persistent symptoms after 280 days (SD 68) post infection. 25% of participants were free from symptoms after 18 days [quartiles: 14, 21]. Factors associated with prolonged time to symptom-free were age 49-59 years compared to < 49 years (aHR 0.70, 95% CI 0.56-0.87), female sex (aHR 0.78, 95% CI 0.65-0.93), lower educational level (aHR 0.77, 95% CI 0.64-0.93), living with a partner (aHR 0.81, 95% CI 0.66-0.99), low resilience (aHR 0.65, 95% CI 0.47-0.90), steroid treatment (aHR 0.22, 95% CI 0.05-0.90) and no medication (aHR 0.74, 95% CI 0.62-0.89) during acute infection.In the studied population, COVID-19 symptoms had resolved in one-quarter of participants within 18 days, and in 34.5% within 28 days. Over half of the participants reported COVID-19-related symptoms 9 months after infection. Symptom persistence was predominantly determined by participant's characteristics that are difficult to modify.Keywords:
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Introduction to Survival Analysis.- Kaplan-Meier Survival Curves and the Log-Rank Test.- The Cox Proportional Hazards Model and Its Characteristics.- Evaluating the Proportional Hazards Assumption.- The Stratified Cox Procedure.- Extension of the Cox Proportional Hazards Model for Time-Dependent Variables.- Parametric Survival Models.- Recurrent Events Survival Analysis.- Competing Risks Survival Analysis.
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Background Although most people with relapsing onset multiple sclerosis (R-MS) eventually transition to secondary progressive multiple sclerosis (SPMS), little is known about disability progression in SPMS. Methods All R-MS patients in the Cardiff MS registry were included. Cox proportional hazards regression was used to examine a) hazard of converting to SPMS and b) hazard of attaining EDSS 6.0 and 8.0 in SPMS. Results 1611 R-MS patients were included. Older age at MS onset (hazard ratio [HR] 1.02, 95%CI 1.01–1.03), male sex (HR 1.71, 95%CI 1.41–2.08), and residual disability after onset (HR 1.38, 95%CI 1.11–1.71) were asso- ciated with increased hazard of SPMS. Male sex (EDSS 6.0 HR 1.41 [1.04–1.90], EDSS 8.0 HR 1.75 [1.14–2.69]) and higher EDSS at SPMS onset (EDSS 6.0 HR 1.31 [1.17–1.46]; EDSS 8.0 HR 1.38 [1.19–1.61]) were associated with increased hazard of reaching disability milestones, while older age at SPMS was associated with a lower hazard of progression (EDSS 6.0 HR 0.94 [0.92–0.96]; EDSS 8.0: HR 0.92 [0.90–0.95]). Conclusions Different factors are associated with hazard of SPMS compared to hazard of disability progres- sion after SPMS onset. These data may be used to plan services, and provide a baseline for comparison for future interventional studies and has relevance for new treatments for SPMS RobertsonNP@cardiff.ac.uk
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A common objective in many medical studies is to investigate the survival time of an individual after being diagnosed with a particular disease or health related condition. In most survival analysis studies the analysis is based on modeling the probability of survival. One of the goals in a survival analysis is usually to model the survival function. This chapter presents three different approaches for modeling a survival function. They are the Kaplan–Meier method of modeling a survival function, the Cox proportional hazards model for a survivor function, and the use of logistic regression for modeling a binary survival response variable. It is important to note that the proportional hazards model is based on the actual survival times and the explanatory variables, a proportional hazards model will provide more information about the survival probabilities than will either the Kaplan–Meier model or a logistic regression model.
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To evaluate the predictive value of white blood cell count (WBC) for short and long term mortality in patients with non-ST elevation acute coronary syndromes (NSTACS) treated with a very early invasive strategy.Prospective cohort study in 1391 consecutive patients with NSTACS undergoing very early revascularisation. Patients were stratified according to quartiles of WBC determined on admission.Kaplan-Meier survival analysis showed a cumulative three year survival of 93.8% in the first quartile of WBC (< 6800/mm(3)), 94.4% in the second quartile (6800-8000/mm(3)), 95.1% in the third quartile (8000-10000/mm(3)), and 82.4% in the fourth quartile (> 10000/mm(3)) at 36 months (p < 0.001 by log rank). Relative to patients in the three lower WBC quartiles, patients in the highest quartile were three times more likely to die during the hospitalisation (hazard ratio 3.2, 95% confidence interval (CI) 1.5 to 7.1; p = 0.003) and during long term follow up (hazard ratio 3.4, 95% CI 2.2 to 5.3; p < 0.001). By multivariate Cox regression analysis including baseline demographic, clinical, and angiographic covariables, WBC in the highest quartile remained a strong independent predictor of mortality (hazard ratio 3.3, 95% CI 1.9 to 5.6; p < 0.001).WBC is a strong independent predictor of short and long term mortality after NSTACS treated with very early revascularisation.
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Background: The National Kidney Foundation–Kidney Disease Outcomes Quality Initiative recommends that the serum aluminum level (SAL) should be below 20 µg/L for patients with maintenance hemodialysis (MHD). However, serum aluminum may have toxic effects on MHD patients even when it is in the apparently acceptable range (below 20 µg/L). Methods: The Medical Ethics Committee approved this study. Initially, 954 MHD patients in dialysis centers were recruited. A total of 901 patients met the inclusion criteria and were followed-up for 1 year. Patients were stratified by SAL into four equal-sized groups: first quartile (<6 µg/L), second quartile (6–9 µg/L), third quartile (9–13 µg/L), and fourth quartile (>13 µg/L). Demographic, biochemical, and dialysis-related data were obtained for analyses. A linear regression model was applied to identify factors associated with SAL. Cox proportional hazard model was used to determine the significance of variables in prediction of mortality. Results: Only 9.3% of MHD patients had SALs of 20 µg/L or more. At the end of the follow-up, 54 patients (6%) died, and the main cause of death was cardiovascular disease. Kaplan–Meier survival analysis showed that patients in the fourth SAL quartile had higher mortality than those in the first SAL quartile (log rank test, χ 2 =13.47, P =0.004). Using the first quartile as reference, Cox multivariate analysis indicated that patients in the third quartile (hazard ratio =1.31, 95% confidence interval =1.12–1.53, P =0.038) and the fourth quartile (hazard ratio =3.19, 95% confidence interval =1.08–8.62, P =0.048) had increased risk of all-cause mortality. Conclusion: This study demonstrates that SAL, even when in an apparently acceptable range (below 20 µg/L), is associated with increased mortality in MHD patients. The findings suggest that avoiding exposure of aluminum as much as possible is warranted for MHD patients. Keywords: aluminum, mortality, hemodialysis
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The hazard ratio and median survival time are the routine indicators in survival analysis. We briefly introduced the relationship between hazard ratio and median survival time and the role of proportional hazard assumption. We compared 110 pairs of hazard ratio and median survival time ratio in 58 articles and demonstrated the reasons for the difference by examples. The results showed that the hazard ratio estimated by the Cox regression model is unreasonable and not equivalent to median survival time ratio when the proportional hazard assumption is not met. Therefore, before performing the Cox regression model, the proportional hazard assumption should be tested first. If proportional hazard assumption is met, Cox regression model can be used; if proportional hazard assumption is not met, restricted mean survival times is suggested.风险比（hazard ratio，HR）和中位生存时间是生存分析时的常规分析和报告指标。本文简要介绍了HR和中位生存时间的关系以及比例风险假定在这两者之间的作用，分析了检索出的58篇文献中的110对风险比和中位生存时间比的差异，并通过实例阐明了产生这种差异的原因。结果表明，在不满足比例风险假定时，Cox回归模型计算得到的风险比是不合理的，且与中位生存时间之比不等价。因此，在使用Cox回归模型前，应先进行比例风险假定的检验，只有符合比例风险假定时才能使用该模型；当不符合比例风险假定时，建议使用限制性平均生存时间。.
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Objective: It is uncertain whether high baseline uric acid (UA) or change in UA concentration over time is related to development of incident hypertension. To investigate relationships between: a) baseline serum uric acid concentration and b) change in UA concentration and incident hypertension. Design and Method: 96,606 Korean individuals (with follow up UA data available for 56,085 people) participating in a health check program was undertaken. Cox regression models were used to estimate adjusted hazard ratios (aHRs) and 95% confidence intervals (CI) for incident hypertension comparing baseline UA quartiles to the lowest UA quartile, and comparing individuals with an increase in UA to those with a decrease in UA concentration over time. Results: 96606 individuals were followed for up to 8 years (median follow-up 3.3 years; IQR, 1.9 to 5.1). 10,405 cases of incident hypertension occurred. In the fully adjusted regression model, there was a significant increase in aHR across increasing UA quartiles at baseline (p value for trend < 0.001 and 0.001 in men and women, respectively), with aHRs comparing the highest to the lowest UA quartiles of 1.29 (95% CI 1.20, 1.39) in men and 1.24 (95% CI 1.09, 1.42) in women. Additionally, stable or increasing UA concentration over time was associated with increased risk of incident hypertension, particularly in participants with baseline UA concentration ≥median (aHRs 1.14 [95% 1.03–1.26] and 1.17 [95% 0.98–1.40] in men and women, respectively). Conclusions: High initial UA concentration and increases in UA concentration over time should be considered independent risk factors for hypertension.
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