The current application of the Royston-Parmar model for prognostic modeling in health research: a scoping review

Timing to First Whole Blood Transfusion and Survival Following Severe Hemorrhage in Trauma Patients

Importance

Civilian trauma centers have revived interest in whole-blood (WB) resuscitation for patients with life-threatening bleeding. However, there remains insufficient evidence that the timing of WB transfusion when given as an adjunct to a massive transfusion protocol (MTP) is associated with a difference in patient survival outcome.

Objective

To evaluate whether earlier timing of first WB transfusion is associated with improved survival at 24 hours and 30 days for adult trauma patients presenting with severe hemorrhage.

Design, Setting, and Participants

This retrospective cohort study used the American College of Surgeons Trauma Quality Improvement Program databank from January 1, 2019, to December 31, 2020, for adult patients presenting to US and Canadian adult civilian level 1 and 2 trauma centers with systolic blood pressure less than 90 mm Hg, with shock index greater than 1, and requiring MTP who received a WB transfusion within the first 24 hours of emergency department (ED) arrival. Patients with burns, prehospital cardiac arrest, deaths within 1 hour of ED arrival, and interfacility transfers were excluded. Data were analyzed from January 3 to October 2, 2023.

Exposure

Patients who received WB as an adjunct to MTP (earlier) compared with patients who had yet to receive WB as part of MTP (later) at any given time point within 24 hours of ED arrival.

Main Outcomes and Measures

Primary outcomes were survival at 24 hours and 30 days.

Results

A total of 1394 patients met the inclusion criteria (1155 male [83%]; median age, 39 years [IQR, 25-51 years]). The study cohort included profoundly injured patients (median Injury Severity Score, 27 [IQR, 17-35]). A survival curve demonstrated a difference in survival within 1 hour of ED presentation and WB transfusion. Whole blood transfusion as an adjunct to MTP given earlier compared with later at each time point was associated with improved survival at 24 hours (adjusted hazard ratio, 0.40; 95% CI, 0.22-0.73; P = .003). Similarly, the survival benefit of earlier WB transfusion remained present at 30 days (adjusted hazard ratio, 0.32; 95% CI, 0.22-0.45; P < .001).

Conclusions and Relevance

In this cohort study, receipt of a WB transfusion earlier at any time point within the first 24 hours of ED arrival was associated with improved survival in patients presenting with severe hemorrhage. The survival benefit was noted shortly after transfusion. The findings of this study are clinically important as the earlier timing of WB administration may offer a survival advantage in actively hemorrhaging patients requiring MTP.

Clinical value of the red blood cell distribution width to albumin ratio in the assessment of prognosis in critically ill patients with sepsis: a retrospective analysis

Background

Red blood cell (RBC) distribution width (RDW) to albumin ratio is a novel biomarker and its prognostic effect on critically ill patients with sepsis has not been extensively investigated. The objective of this study was to identify the prognostic value of the RDW to albumin ratio in these patients.

Methods

Data were extracted from the Medical Information Mart for Intensive Care III (MIMIC-III) database. A Cox proportional hazards model and restricted cubic spline model were used to determine the association of RDW to albumin ratio with mortality. Receiver operating characteristic (ROC) curves and Kaplan-Meier survival curves were applied, and the area under the curve (AUC) was used to compare the predictive value.

Results

A total of 3,969 eligible patients were enrolled. The median RDW to albumin ratio was significantly higher in non-survivors than in survivors at 30 and 90 days. Patients were divided into groups according to the RDW to albumin ratio, and the risk of 30- and 90-day mortality markedly increased in the group with a higher ratio. The relationship between the RDW to albumin ratio as a continuous variable and 30-day mortality also showed an upward trend in the restricted cubic spline. The AUC of the RDW to albumin ratio was 0.633 in discriminating 30-day mortality which was similar to that of the lactate to albumin ratio (AUC =0.617; P=0.133) and higher than that of the neutrophil percentage to albumin ratio (AUC =0.559; P<0.001).

Conclusions

The RDW to albumin ratio is a promising biomarker for assessing the prognosis of critically ill patients with sepsis. Its predictive value in determining mortality was found to be similar to that of the lactate to albumin ratio and superior to that of the neutrophil percentage to albumin ratio.

The PSY-SIM Model: Using Real-World Data to Inform Health Care Policy for Individuals With Chronic Psychotic Disorders

BACKGROUND AND HYPOTHESIS

Few microsimulation models have been developed for chronic psychotic disorders, severe and disabling mental disorders associated with poor medical and psychiatric outcomes, and high costs of care. The objective of this work was to develop a microsimulation model for individuals with chronic psychotic disorders and to use the model to examine the impact of a smoking cessation initiative on patient outcomes.

STUDY DESIGN

Using health records and survey data from Ontario, Canada, the PSY-SIM model was developed to simulate health and cost outcomes of individuals with chronic psychotic disorders. The model was then used to examine the impact of the Smoking Treatment for Ontario Patients (STOP) program from Ontario on the development of chronic conditions, life expectancy, quality of life, and lifetime health care costs.

STUDY RESULTS

Individuals with chronic psychotic disorders had a lifetime risk of 63% for congestive heart failure and roughly 50% for respiratory disease, cancer and diabetes, and a life expectancy of 76 years. The model suggests the STOP program can reduce morbidity and lead to survival and quality of life gains with modest increases in health care costs. At a long-term quit rate of 4.4%, the incremental cost-effectiveness ratio of the STOP program was $41,936/QALY compared with status quo.

CONCLUSIONS

Smoking cessation initiatives among individuals with chronic psychotic disorders can be cost-effective. These findings will be relevant for decision-makers and clinicians looking to improving health outcomes among this patient population.

Survival in Young Adults With Cancer Is Associated With Medicaid Expansion Through the Affordable Care Act

PURPOSE

Medicaid expansion through the Affordable Care Act (ACA) has been shown to improve insurance coverage and early diagnosis of cancer in young adults (YAs); whether these improvements translate to survival benefits remains unknown. We examined the association between Medicaid expansion under the ACA and 2-year overall survival among YAs with cancer.

METHODS

Using the National Cancer Database, we identified 345,413 YAs (age 18-39 years) diagnosed with cancer in 2010-2017. We applied the difference-in-differences (DD) method to estimate changes in 2-year overall survival after versus before Medicaid expansion in expansion versus nonexpansion states.

RESULTS

Among all YAs, 2-year overall survival increased more in expansion states (90.39% pre-expansion to 91.85% postexpansion) than in nonexpansion states (88.98% pre-expansion to 90.07% postexpansion), resulting in a net increase of 0.55 percentage points (ppt; 95% CI, 0.13 to 0.96). The expansion-associated survival benefit was concentrated in patients with female breast cancer (DD, 1.20 ppt; 95%CI, 0.27 to 2.12) when stratifying by cancer type and in patients with stage IV disease (DD, 2.56; 95%CI, 0.36 to 4.77) when stratifying by stage. In addition, greater survival benefit associated with Medicaid expansion was observed among racial and ethnic minoritized groups (DD, 1.01 ppt; 95% CI, 0.14 to 1.87) as compared with non-Hispanic White peers (DD, 0.41 ppt; 95% CI, -0.06 to 0.87) and among patients with a Charlson comorbidity score of ≥ 2 (DD, 6.48 ppt; 95% CI, 0.81 to 12.16) than those with a comorbidity score of 0 (DD, 0.44 ppt; 95% CI, 0.005 to 0.87).

CONCLUSION

Medicaid expansion under the ACA was associated with an improvement in overall survival among YAs with cancer, with survival benefits most pronounced among patients of under-represented race and ethnicity and patients with high-risk diseases.

Transcatheter and Surgical Aortic Valve Replacement in Patients With Bicuspid Aortic Valve Stenosis

BACKGROUND

Transcatheter aortic valve replacement (TAVR) is not widely used in patients with bicuspid aortic valve (BAV) disease and has not yet been studied in randomized clinical trials. We characterized the rate of use and outcomes of TAVR and surgical aortic valve replacement (SAVR) in patients with BAV.

METHODS

Adults with BAV stenosis receiving SAVR or TAVR procedures were abstracted from the 2012 to 2019 Nationwide Readmissions Database (NRD). Risk-adjusted analyses were performed with NRD-provided weights and inverse probability of treatment weights (IPTW) to examine the association of treatment strategy on inpatient mortality, complications, and hospitalization resource utilization. Nonelective readmissions within 90 days of discharge and reintervention at the first readmission were also examined.

RESULTS

Of an estimated 56 331 patients with BAV requiring aortic valve replacement, 6.8% underwent TAVR. Unadjusted analysis demonstrated higher index hospitalization mortality for TAVR compared with SAVR. Upon risk adjustment using NRD-provided weights, the odds of pacemaker implantation remained significantly higher for TAVR patients compared with SAVR, with no significant difference in mortality. When NRD-provided survey weights were applied, TAVR had higher rates of 90-day readmission. Adjustment with inverse probability of treatment weights resolved these differences between the 2 groups. Regardless of the risk-adjustment method, the odds of reintervention were consistently higher among BAV TAVR patients compared with SAVR.

CONCLUSIONS

The present analysis demonstrates comparable in-hospital mortality and morbidity for TAVR and SAVR patients in the moderate-risk era. With increasing TAVR use in BAV, surgeons must further refine selection criteria with consideration of concomitant aortopathy and implications of reintervention.

Assessing Performance and Clinical Usefulness in Prediction Models With Survival Outcomes: Practical Guidance for Cox Proportional Hazards Models

Risk prediction models need thorough validation to assess their performance. Validation of models for survival outcomes poses challenges due to the censoring of observations and the varying time horizon at which predictions can be made. This article describes measures to evaluate predictions and the potential improvement in decision making from survival models based on Cox proportional hazards regression.

As a motivating case study, the authors consider the prediction of the composite outcome of recurrence or death (the "event") in patients with breast cancer after surgery. They developed a simple Cox regression model with 3 predictors, as in the Nottingham Prognostic Index, in 2982 women (1275 events over 5 years of follow-up) and externally validated this model in 686 women (285 events over 5 years). Improvement in performance was assessed after the addition of progesterone receptor as a prognostic biomarker.

The model predictions can be evaluated across the full range of observed follow-up times or for the event occurring by the end of a fixed time horizon of interest. The authors first discuss recommended statistical measures that evaluate model performance in terms of discrimination, calibration, or overall performance. Further, they evaluate the potential clinical utility of the model to support clinical decision making according to a net benefit measure. They provide SAS and R code to illustrate internal and external validation.

The authors recommend the proposed set of performance measures for transparent reporting of the validity of predictions from survival models.

An analysis of factors associated with neonatal, post-neonatal and child mortality in Haiti, including breastfeeding as a time-dependent variable

AIM

The main objective of the study was to identify factors associated with neonatal, post-neonatal and child mortality. The study also investigated breastfeeding status as a time-dependent variable.

METHODS

The 2016-2017 Haitian Demographic and Health Survey was analysed. The analysis was done on 6530 live births. Time-constant and time-dependent multivariable Royston-Parmar spline models were used to identify associated factors for all three age groups. Restricted mean survival times were calculated for the different levels of the breastfeeding variable for each age group.

RESULTS

Neonates and post-neonates who were not breastfed were associated with increased mortality, hazard ratio (HR) 22.13 (95% confidence interval (CI), 16.40-29.87) and HR 4.99 (95% CI, 3.29-7.56), respectively. Males in the child age group were associated with increased mortality, HR 2.04 (95% CI, 1.29-3.23) and HR 2.03 (95% CI, 1.28-3.21) under the time-constant and time-dependent models, respectively.

CONCLUSIONS

Early initiation of breastfeeding and breastfeeding throughout the post-neonatal period is recommended. Outreach programmes that provide support and education for vulnerable families are also recommended.

A comparison of survival models for prediction of eight-year revision risk following total knee and hip arthroplasty

Background

There is increasing interest in the development and use of clinical prediction models, but a lack of evidence-supported guidance on the merits of different modelling approaches. This is especially true for time-to-event outcomes, where limited studies have compared the vast number of modelling approaches available. This study compares prediction accuracy and variable importance measures for four modelling approaches in prediction of time-to-revision surgery following total knee arthroplasty (TKA) and total hip arthroplasty (THA).

Methods

The study included 321,945 TKA and 151,113 THA procedures performed between 1 January 2003 and 31 December 2017. Accuracy of the Cox model, Weibull parametric model, flexible parametric model, and random survival forest were compared, with patient age, sex, comorbidities, and prosthesis characteristics considered as predictors. Prediction accuracy was assessed using the Index of Prediction Accuracy (IPA), c-index, and smoothed calibration curves. Variable importance rankings from the Cox model and random survival forest were also compared.

Results

Overall, the Cox and flexible parametric survival models performed best for prediction of both TKA (integrated IPA 0.056 (95% CI [0.054, 0.057]) compared to 0.054 (95% CI [0.053, 0.056]) for the Weibull parametric model), and THA revision. (0.029 95% CI [0.027, 0.030] compared to 0.027 (95% CI [0.025, 0.028]) for the random survival forest). The c-index showed broadly similar discrimination between all modelling approaches. Models were generally well calibrated, but random survival forest underfitted the predicted risk of TKA revision compared to regression approaches. The most important predictors of revision were similar in the Cox model and random survival forest for TKA (age, opioid use, and patella resurfacing) and THA (femoral cement, depression, and opioid use).

Conclusion

The Cox and flexible parametric models had superior overall performance, although all approaches performed similarly. Notably, this study showed no benefit of a tuned random survival forest over regression models in this setting.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12874-022-01644-3.

Intron 4-5 hTERT DNA Hypermethylation in Merkel Cell Carcinoma: Frequency, Association with Other Clinico-pathological Features and Prognostic Relevance

Merkel cell carcinoma (MCC) is an aggressive skin tumor with neuroendocrine differentiation, mainly affecting elderly population or immunocompromised individuals. As methylation of the human telomerase reverse transcriptase (mhTERT) has been shown to be a prognostic factor in different tumors, we investigated its role in MCC, in particular in intron 4-5 where rs10069690 has been mapped and recognized as a cancer susceptibility locus. DNA methylation analysis of hTERT gene was assessed retrospectively in a cohort of 69 MCC patients from the University of Bologna, University of Turin and University of Insubria. Overall mortality was evaluated with Kaplan-Meier curves and multivariable Royston-Parmar models. High levels of mhTERT (mhTERT_high) (HR = 2.500, p = 0.015) and p63 (HR = 2.659, p = 0.016) were the only two clinico-pathological features significantly associated with a higher overall mortality at the multivariate analysis. We did not find different levels of mhTERT between MCPyV (+) and (-) cases (21 vs 14, p = 0.554); furthermore, mhTERT_high was strongly associated with older age (80.5 vs 72 years, p = 0.026), no angioinvasion (40.7% vs 71.0%, p = 0.015), lower Ki67 (50 vs 70%, p = 0.005), and PD-L1 expressions in both tumor (0 vs 3%, p = 0.021) and immune cells (0 vs 10%, p = 0.002). mhTERT is a frequently involved epigenetic mechanism and a relevant prognostic factor in MCC. In addition, it belongs to the shared oncogenic pathways of MCC (MCPyV and UV-radiations) and it could be crucial, together with other epigenetic and genetic mechanisms as gene amplification, in determining the final levels of hTERT mRNA and telomerase activity in these patients.

Comparison of the performances of survival analysis regression models for analysis of conception modes and risk of type-1 diabetes among 1985-2015 Swedish birth cohort

The goal is to examine the risk of conception mode-type-1 diabetes using different survival analysis modelling approaches and examine if there are differentials in the risk of type-1 diabetes between children from fresh and frozen-thawed embryo transfers. We aimed to compare the performances and fitness of different survival analysis regression models with the Cox proportional hazard (CPH) model used in an earlier study. The effect of conception modes and other prognostic factors on type-1 diabetes among children conceived either spontaneously or by assisted reproductive technology (ART) and its sub-groups was modelled in the earlier study. We used the information on all singleton children from the Swedish Medical Birth Register hosted by the Swedish National Board of Health and Welfare, 1985 to 2015. The main explanatory variable was the mode of conception. We applied the CPH, parametric and flexible parametric survival regression (FPSR) models to the data at 5% significance level. Loglikelihood, Akaike and Bayesian information criteria were used to assess model fit. Among the 3,138,540 singletons, 47,938 (1.5%) were conceived through ART (11,211 frozen-thawed transfer and 36,727 fresh embryo transfer). In total, 18,118 (0.58%) of the children had type-1 diabetes, higher among (0.58%) those conceived spontaneously than the ART-conceived (0.42%). The median (Interquartile range (IQR)) age at onset of type-1 diabetes among spontaneously conceived children was 10 (14-6) years, 8(5-12) for ART, 6 (4-10) years for frozen-thawed embryo transfer and 9 (5-12) years for fresh embryo transfer. The estimates from the CPH, FPSR and parametric PH models are similar. There was no significant difference in the risk of type-1 diabetes among ART- and spontaneously conceived children; FPSR: (adjusted Hazard Ratio (aHR) = 1.070; 95% Confidence Interval (CI):0.929-1.232, p = 0.346) vs CPH: (aHR = 1.068; 95%CI: 0.927-1.230, p = 0.361). A sub-analysis showed that the adjusted hazard of type-1 diabetes was 37% (aHR = 1.368; 95%CI: 1.013-1.847, p = 0.041) higher among children from frozen-thawed embryo transfer than among children from spontaneous conception. The hazard of type-1 diabetes was higher among children whose mothers do not smoke (aHR = 1.296; 95%CI:1.240-1.354, p<0.001) and of diabetic mothers (aHR = 6.419; 95%CI:5.852-7.041, p<0.001) and fathers (aHR = 8.808; 95%CI:8.221-9.437, p<0.001). The estimates from the CPH, parametric models and the FPSR model were close. This is an indication that the models performed similarly and any of them can be used to model the data. We couldn't establish that ART increases the risk of type-1 diabetes except when it is subdivided into its two subtypes. There is evidence of a greater risk of type-1 diabetes when conception is through frozen-thawed transfer.

Heart Rate Fluctuation and Mortality in Critically Ill Myocardial Infarction Patients: A Retrospective Cohort Study

Background: Whether heart rate (HR) fluctuation after admission has an impact on the outcomes of critically ill myocardial infarction (MI) patients in intensive care unit remains unknown. Methods: A total of 2,031 MI patients were enrolled from the Medical Information Mart for Intensive Care (MIMIC-III) database. HR fluctuation was calculated as the maximum HR minus the minimum HR in the initial 24 h after admission. Participants were divided into 3 groups, namely, low HR fluctuation [<30 beats per minute (bpm)], medium HR fluctuation (30-49 bpm), and high HR fluctuation (≥ 50 bpm). The main outcomes were 30-day and 1-year mortality. Cox regression and restricted cubic spline model were used. Results: Each 10-bpm increase in HR fluctuation was associated with a higher risk of 30-day mortality and 1-year mortality, with adjusted hazard ratios of 1.122 (95% CI, 1.083-1.162) and 1.107 (95% CI, 1.074-1.140), respectively. Compared with the low HR fluctuation group, the high HR fluctuation group suffered a significantly higher risk of mortality after adjustment, with hazard ratios of 2.156 (95% CI, 1.483-3.134) for 30-day mortality and 1.796 (95% CI, 1.354-2.381) for 1-year mortality. A typical J-type curve was observed in restricted cubic splines for the association between HR fluctuation and 30-day or 1-year mortality of MI patients, with the lowest risk on the HR fluctuation of 30 bpm. Sensitivity analyses emphasized the robustness of our results. Conclusions: This retrospective cohort study revealed an independent positive association between HR fluctuation and 30-day and 1-year mortality in critically ill MI patients, which warrants further investigation.

Gemcitabine/nab-Paclitaxel versus FOLFIRINOX for palliative first-line treatment of advanced pancreatic cancer: A propensity score analysis

BACKGROUND

Gemcitabine/nab-paclitaxel (GN) and FOLFIRINOX are standard first-line treatment options for advanced pancreatic ductal adenocarcinoma (aPDAC), but currently no prospective randomised head-to-head comparison between these treatments has yet been performed.

METHODS

We conducted a comparative propensity score (PS) analysis of overall (OS) and progression-free survival (PFS) in a tri-centre cohort of patients with aPDAC undergoing palliative first-line treatment with either GN or FOLFIRINOX.

RESULTS

In unadjusted analysis, OS and PFS were highly similar between patients treated with GN (n = 297) and FOLFIRINOX (n = 158). In detail, median, 1- and 2-year OS estimates were 10.1 months, 42% and 18% in the GN group, as compared to 11.2 months, 45% and 12% in the FOLFIRINOX group, respectively (log-rank p = 0.783). Accordingly, median (4.6 versus 4.8 months), 6-month (40% versus 43%) and 1-year (9% versus 9%) PFS estimates did not significantly differ (log-rank p = 0.717). However, patients treated with FOLFIRINOX were significantly younger, had fewer comorbidities, and a better Eastern Cooperative Oncology Group performance status. These imbalances were accounted for by weighting the data with the PS. In PS analysis of survival outcomes, OS and PFS remained comparable between the two treatment groups. In detail, PS-weighted median, 1- and 2-year OS estimates were 10.1 months, 42% and 18% in the GN group, as compared to 10.1 months, 40% and 13% in the FOLFIRINOX group (PS-weighted log-rank p = 0.449). PS-weighted PFS estimates again did not differ (PS-weighted log-rank p = 0.329).

CONCLUSION

This real-world comparative effectiveness study indicates that FOLFIRINOX and GN have similar effectiveness in the palliative first-line treatment of aPDAC.

PlGF (Placental Growth Factor) Testing in Clinical Practice: Evidence From a Canadian Tertiary Maternity Referral Center

[Figure: see text].

Individual participant data meta-analysis for external validation, recalibration, and updating of a flexible parametric prognostic model

Individual participant data (IPD) from multiple sources allows external validation of a prognostic model across multiple populations. Often this reveals poor calibration, potentially causing poor predictive performance in some populations. However, rather than discarding the model outright, it may be possible to modify the model to improve performance using recalibration techniques. We use IPD meta-analysis to identify the simplest method to achieve good model performance. We examine four options for recalibrating an existing time-to-event model across multiple populations: (i) shifting the baseline hazard by a constant, (ii) re-estimating the shape of the baseline hazard, (iii) adjusting the prognostic index as a whole, and (iv) adjusting individual predictor effects. For each strategy, IPD meta-analysis examines (heterogeneity in) model performance across populations. Additionally, the probability of achieving good performance in a new population can be calculated allowing ranking of recalibration methods. In an applied example, IPD meta-analysis reveals that the existing model had poor calibration in some populations, and large heterogeneity across populations. However, re-estimation of the intercept substantially improved the expected calibration in new populations, and reduced between-population heterogeneity. Comparing recalibration strategies showed that re-estimating both the magnitude and shape of the baseline hazard gave the highest predicted probability of good performance in a new population. In conclusion, IPD meta-analysis allows a prognostic model to be externally validated in multiple settings, and enables recalibration strategies to be compared and ranked to decide on the least aggressive recalibration strategy to achieve acceptable external model performance without discarding existing model information.

Heart rate fluctuation predicts mortality in critically ill patients in the intensive care unit: a retrospective cohort study

Background

To evaluate the association between heart rate (HR) fluctuation and mortality in critically ill patients in the intensive care unit (ICU).

Methods

A total of 27,814 patients were enrolled from the Medical Information Mart for Intensive Care database and were divided into 3 groups: low HR fluctuation [<25 beats per minute (bpm)], control (25-34 bpm), and high HR fluctuation (≥35 bpm), based on the initial 24-hour HR fluctuation (calculated as the maximum HR minus minimum HR). Multivariate Cox regression and restricted cubic spline models were used.

Results

Compared to the control group, higher risk of 28-day and 1-year mortality remained significant in an adjusted model, with hazard ratios of 1.210 [95% confidence interval (CI), 1.103-1.327] and 1.150 (95% CI, 1.078-1.227), respectively, in the high HR fluctuation group, as well as hazard ratios of 1.130 (95% CI, 1.035-1.232) and 1.087 (95% CI, 1.022-1.157), respectively, in the low HR fluctuation group. Restricted cubic splines showed a U-type curve, with the lowest risk of mortality at an HR fluctuation of 30 bpm.

Conclusions

This retrospective cohort study revealed that both high and low HR fluctuation correlated with increased mortality in critically ill ICU patients, providing new insights for optimizing HR control strategies.

Performance evaluation of survival regression models in analysing Swedish dental implant complication data with frailty

The use of inappropriate methods for estimating the effects of covariates in survival data with frailty leads to erroneous conclusions in medical research. This study evaluated the performance of 13 survival regression models in assessing the factors associated with the timing of complications in implant-supported dental restorations in a Swedish cohort. Data were obtained from randomly selected cohort (n = 596) of Swedish patients provided with dental restorations supported in 2003. Patients were evaluated over 9 years of implant loss, peri-implantitis or technical complications. Best Model was identified using goodness, AIC and BIC. The loglikelihood, the AIC and BIC were consistently lower in flexible parametric model with frailty (df = 2) than other models. Adjusted hazard of implant complications was 45% (adjusted Hazard Ratio (aHR) = 1.449; 95% Confidence Interval (CI): 1.153-1.821, p = 0.001) higher among patients with periodontitis. While controlling for other variables, the hazard of implant complications was about 5 times (aHR = 4.641; 95% CI: 2.911-7.401, p<0.001) and 2 times (aHR = 2.338; 95% CI: 1.553-3.519, p<0.001) higher among patients with full- and partial-jaw restorations than those with single crowns. Flexible parametric survival model with frailty are the most suitable for modelling implant complications among the studied patients.

A study protocol for a predictive algorithm to assess population-based premature mortality risk: Premature Mortality Population Risk Tool (PreMPoRT)

BACKGROUND

Premature mortality is an important population health indicator used to assess health system functioning and to identify areas in need of health system intervention. Predicting the future incidence of premature mortality in the population can facilitate initiatives that promote equitable health policies and effective delivery of public health services. This study protocol proposes the development and validation of the Premature Mortality Risk Prediction Tool (PreMPoRT) that will predict the incidence of premature mortality using large population-based community health surveys and multivariable modeling approaches.

METHODS

PreMPoRT will be developed and validated using various training, validation, and test data sets generated from the six cycles of the Canadian Community Health Survey (CCHS) linked to the Canadian Vital Statistics Database from 2000 to 2017. Population-level risk factor information on demographic characteristics, health behaviors, area level measures, and other health-related factors will be used to develop PreMPoRT and to predict the incidence of premature mortality, defined as death prior to age 75, over a 5-year period. Sex-specific Weibull accelerated failure time models will be developed using a Canadian provincial derivation cohort consisting of approximately 500,000 individuals, with approximately equal proportion of males and females, and about 12,000 events of premature mortality. External validation will be performed using separate linked files (CCHS cycles 2007-2008, 2009-2010, and 2011-2012) from the development cohort (CCHS cycles 2000-2001, 2003-2004, and 2005-2006) to check the robustness of the prediction model. Measures of overall predictive performance (e.g., Nagelkerke's R2), calibration (e.g., calibration plots), and discrimination (e.g., Harrell's concordance statistic) will be assessed, including calibration within defined subgroups of importance to knowledge users and policymakers.

DISCUSSION

Using routinely collected risk factor information, we anticipate that PreMPoRT will produce population-based estimates of premature mortality and will be used to inform population strategies for prevention.

A nationwide, retrospective, data-linkage, cohort study of epilepsy and incident dementia

OBJECTIVE

To determine the association of epilepsy with incident dementia by conducting a nationwide, retrospective data-linkage, cohort study to examine whether the association varies according to dementia subtypes and to investigate whether risk factors modify the association.

METHODS

We used linked health data from hospitalization, mortality records, and primary care consultations to follow up 563,151 Welsh residents from their 60th birthday to estimate dementia rate and associated risk factors. Dementia, epilepsy, and covariates (medication, smoking, comorbid conditions) were classified with the use of previously validated code lists. We studied rate of dementia and dementia subtypes in people with epilepsy (PWE) and without epilepsy using (stratified) Kaplan-Meier plots and flexible parametric survival models.

RESULTS

PWE had a 2.5 (95% confidence interval [CI] 2.3-2.6) times higher hazard of incident dementia, a 1.6 (95% CI 1.4-1.8) times higher hazard of incident Alzheimer disease (AD), and a 3.1 (95% CI 2.8-3.4) times higher hazard of incident Vascular dementia (VaD). A history of stroke modified the increased incidence in PWE. PWE who were first diagnosed at ≤25 years of age had a dementia rate similar to that of those diagnosed later in life. PWE who had ever been prescribed sodium valproate compared to those who had not were at higher risk of dementia (hazard ratio [HR] 1.6, 99% CI 1.4-1.9) and VaD (HR 1.7, 99% CI 1.4-2.1) but not AD (HR 1.2, 99% CI 0.9-1.5).

CONCLUSION

PWE compared to those without epilepsy have an increased dementia risk.

PD-1 (PDCD1) promoter methylation in Merkel cell carcinoma: prognostic relevance and relationship with clinico-pathological parameters

Merkel cell carcinoma is an aggressive neuroendocrine skin tumor, for which several non-conclusive prognostic factors of adverse clinical behavior have been reported. As promoter methylation of the immune checkpoint receptor CD279/PD-1/PDCD1(mPDCD1) has been shown to be a prognostic factor in different cancers, we investigated its role in Merkel cell carcinoma. mPDCD1was assessed retrospectively in a cohort of 69 Merkel cell carcinoma patients from the University of Bologna, University of Turin and University of Insubria. Kaplan-Meier curves and log-rank tests were calculated for all variables. To assess the influence of mPDCD1, the Cox proportional hazards model and different Royston-Parmar models were evaluated. High PDCD1 methylation (mPDCD1_high) was associated with a higher overall mortality at both the univariate analysis (log rank test: χ² = 5.17, p = 0.023; permutation test: p = 0.023) and the multivariate analysis (HR = 2.111, p = 0.042). The other variables associated with a higher overall mortality at the multivariate analysis were clinical stage III-IV (HR = 2.357, p = 0.008), size > 2 cm (HR = 2.248, p = 0.031) and Merkel cell polyomavirus (HR = 0.397, p = 0.015). Further, mPDCD1_high was strongly associated with older age (81 vs 76 years, p = 0.042), absence of immune cells (92.6%, p < 0.001), no expression of PD-L1 by immune cells (70.4%, p = 0.041) and by both immune and tumor cells (70.4%, p = 0.001). mPDCD1 is a valid prognostic parameter in patients affected by Merkel cell carcinoma. In addition, it could provide an estimate of the global PD-1/PD-L1 expression with potentially relevant implications from a therapeutic point of view.

Chronic Disease Population Risk Tool (CDPoRT): a study protocol for a prediction model that assesses population-based chronic disease incidence

BACKGROUND

Population-based risk prediction tools exist for individual chronic diseases. From a population health perspective, studying chronic diseases together provides a comprehensive view of the burden of disease in the population. Thus, public health officials and health policymakers would benefit from a prediction tool that measures the incidence of chronic diseases compositely. This study protocol proposes the development and validation of the Chronic Disease Population Risk Tool (CDPoRT) that will predict the incidence of six chronic diseases in the population setting using multivariable modeling techniques.

METHODS

CDPoRT will be built using population-based responses to the first six cycles of the Canadian Community Health Survey linked to health administrative data in Ontario and Manitoba from 2000 to 2014. Predictors including modifiable lifestyle risk factors (i.e., alcohol consumption, cigarette smoking, diet, and physical activity) will be used to predict time-to-chronic disease incidence (i.e., congestive heart failure, chronic obstructive pulmonary disease, diabetes, lung cancer, myocardial infarction, and stroke including transient ischemic heart attack). Sex-specific Royston-Parmar models will be used for model development and validation with death free of chronic disease as a competing risk. CDPoRT will be developed using an Ontario derivation cohort consisting of 47,960 females and 38,267 males with 7035 and 6220 chronic disease events, respectively. The model will be validated using split-sample validation using an Ontario validation cohort consisting of 20,325 females and 16,627 males with 2972 and 2658 chronic disease events, respectively. The model will be externally validated in the Manitoba validation cohort (i.e., geographic validation) expected to consist of 11,800 females and 9700 males with 1650 and 1550 chronic disease events, respectively. Measures of overall predictive accuracy (e.g., Nagelkerke's R 2), discrimination (e.g., Harrell's concordance statistic), and calibration (e.g., calibration plots) will be used to assess predictive performance.

DISCUSSION

To the extent of our knowledge, CDPoRT will be the first population-based regression prediction model that will predict the incidence of multiple chronic diseases simultaneously at the population level.