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Animals often respond to climate change with changes in morphology, e.g., shrinking body size with increasing temperatures, as expected by Bergmann’s rule. Because small body size can have fitness costs for individuals, this trend could threaten populations. Recent studies, however, show that morphological responses to climate change and the resulting fitness consequences cannot be generalized even among related species. In this long-term study, we investigate the interaction between ambient temperature, body size and survival probability in a large number of individually marked wild adult female Natterer’s bats (Myotis nattereri). We compare populations from two geographical regions in Germany with a different climate. In a sliding window analysis, we found larger body sizes in adult females that were raised in warmer summers only in the northern population, but not in the southern population that experienced an overall warmer climate. With a capture-mark-recapture approach, we showed that larger individuals had higher survival rates, demonstrating that weather conditions in early life could have long-lasting fitness effects. The different responses in body size to warmer temperatures in the two regions highlight that fitness-relevant morphological responses to climate change have to be viewed on a regional scale and may affect local populations differently.
Background
The care of palliative patients takes place as non-specialized and specialized care, in outpatient and inpatient settings. However, palliative care is largely provided as General Outpatient Palliative Care (GOPC). This study aimed to investigate whether the survival curves of GOPC patients differed from those of the more intensive palliative care modalities and whether GOPC palliative care was appropriate in terms of timing.
Methods
The study is based on claims data from a large statutory health insurance. The analysis included 4177 patients who received palliative care starting in 2015 and who were fully insured 1 year before and 1 year after palliative care or until death. The probability of survival was observed for 12 months. Patients were classified into group A, which consisted of patients who received palliative care only with GOPC, and group B including patients who received inpatient or specialized outpatient palliative care. Group A was further divided into two subgroups. Patients who received GOPC on only 1 day were assigned to subgroup A1, and patients who received GOPC on two or more days were assigned to subgroup A2. The survival analysis was carried out using Kaplan-Meier curves. The median survival times were compared with the log-rank test.
Results
The survival curves differed between groups A and B, except in the first quartile of the survival distribution. The median survival was significantly longer in group A (137 days, n = 2763) than in group B (47 days, n = 1424, p < 0.0001) and shorter in group A1 (35 days, n = 986) than in group A2 (217 days, n = 1767, p < 0.0001). The survival rate during the 12-month follow-up was higher in group A (42%) than in group B (11%) and lower in group A1 (38%) than in group A2 (44%).
Conclusions
The results of the analysis revealed that patients who received the first palliative care shortly before death suspected insufficient care, especially patients who received GOPC for only 1 day and no further palliative care until death or 12-month follow-up. Palliative care should start as early as necessary and be continuous until the end of life.
Background
Previous work has focused on speckle-tracking echocardiography (STE)-derived global longitudinal and circumferential peak strain as potential superior prognostic metric markers compared with left ventricular ejection fraction (LVEF). However, the value of regional distribution and the respective orientation of left ventricular wall motion (quantified as strain and derived from STE) for survival prediction have not been investigated yet. Moreover, most of the recent studies on risk stratification in primary and secondary prevention do not use neural networks for outcome prediction.
Purpose
To evaluate the performance of neural networks for predicting all cause-mortality with different model inputs in a moderate-sized general population cohort.
Methods
All participants of the second cohort of the population-based Study of Health in Pomerania (SHIP-TREND-0) without prior cardiovascular disease (CVD; acute myocardial infarction, cardiac surgery/intervention, heart failure and stroke) and with transthoracic echocardiography exams were followed for all-cause mortality from baseline examination (2008-2012) until 2019.
A novel deep neural network architecture ‘nnet-Surv-rcsplines’, that extends the Royston-Parmar- cubic splines survival model to neural networks was proposed and applied to predict all-cause mortality from STE-derived global and/or regional myocardial longitudinal, circumferential, transverse, and radial strain in addition to the components of the ESC SCORE model. The models were evaluated by 8.5-year area-under-the-receiver-operating-characteristic (AUROC) and (scaled) Brier score [(S)BS]and compared to the SCORE model adjusted for mortality rates in Germany in 2010.
Results
In total, 3858 participants (53 % female, median age 51 years) were followed for a median time of 8.4 (95 % CI 8.3 – 8.5) years. Application of ‘nnet-Surv-rcsplines’ to the components of the ESC SCORE model alone resulted in the best discriminatory performance (AUROC 0.9 [0.86-0.91]) and lowest prediction error (SBS 21[18-23] %). The latter was significantly lower (p <0.001) than the original SCORE model (SBS 11 [9.5 - 13] %), while discrimination did not differ significantly. There was no difference in (S)BS (p= 0.66) when global circumferential and longitudinal strain were added to the model. Solely including STE-data resulted in an informative (AUROC 0.71 [0.69, 0.74]; SBS 3.6 [2.8-4.6] %) but worse (p<0.001) model performance than when considering the sociodemographic and instrumental biomarkers, too.
Conclusion
Regional myocardial strain distribution contains prognostic information for predicting all-cause mortality in a primary prevention sample of subjects without CVD. Still, the incremental prognostic value of STE parameters was not demonstrated. Application of neural networks on available traditional risk factors in primary prevention may improve outcome prediction compared to standard statistical approaches and lead to better treatment decisions.