| 1. |
|
|
| 2. |
|
|
| 3. |
|
|
| 4. |
|
|
| 5. |
|
|
| 6. |
|
|
| 7. |
|
|
| 8. |
|
|
| 9. |
- Mei, J, et al.
(författare)
-
Development and external validation of a COVID-19 mortality risk prediction algorithm: a multicentre retrospective cohort study
- 2020
-
Ingår i: BMJ open. - : BMJ. - 2044-6055. ; 10:12, s. e044028-
-
Tidskriftsartikel (refereegranskat)abstract
- This study aimed to develop and externally validate a COVID-19 mortality risk prediction algorithm.DesignRetrospective cohort study.SettingFive designated tertiary hospitals for COVID-19 in Hubei province, China.ParticipantsWe routinely collected medical data of 1364 confirmed adult patients with COVID-19 between 8 January and 19 March 2020. Among them, 1088 patients from two designated hospitals in Wuhan were used to develop the prognostic model, and 276 patients from three hospitals outside Wuhan were used for external validation. All patients were followed up for a maximal of 60 days after the diagnosis of COVID-19.MethodsThe model discrimination was assessed by the area under the receiver operating characteristic curve (AUC) and Somers’ D test, and calibration was examined by the calibration plot. Decision curve analysis was conducted.Main outcome measuresThe primary outcome was all-cause mortality within 60 days after the diagnosis of COVID-19.ResultsThe full model included seven predictors of age, respiratory failure, white cell count, lymphocytes, platelets, D-dimer and lactate dehydrogenase. The simple model contained five indicators of age, respiratory failure, coronary heart disease, renal failure and heart failure. After cross-validation, the AUC statistics based on derivation cohort were 0.96 (95% CI, 0.96 to 0.97) for the full model and 0.92 (95% CI, 0.89 to 0.95) for the simple model. The AUC statistics based on the external validation cohort were 0.97 (95% CI, 0.96 to 0.98) for the full model and 0.88 (95% CI, 0.80 to 0.96) for the simple model. Good calibration accuracy of these two models was found in the derivation and validation cohort.ConclusionThe prediction models showed good model performance in identifying patients with COVID-19 with a high risk of death in 60 days. It may be useful for acute risk classification.
|
|
| 10. |
- Tian, SL, et al.
(författare)
-
Presence of peripheral arterial disease predicts loss of residual renal function in incident CAPD patients
- 2012
-
Ingår i: Peritoneal dialysis international : journal of the International Society for Peritoneal Dialysis. - : SAGE Publications. - 1718-4304 .- 0896-8608. ; 32:1, s. 67-72
-
Tidskriftsartikel (refereegranskat)abstract
- Accelerated cardiovascular disease (CVD), including peripheral arterial disease (PAD), is very common in patients with end-stage renal disease. Residual renal function (RRF) is a strong predictor of patient survival that is suggested to be linked to the degree of CVD. However, the relationship between PAD and decline in RRF has not previously been measured. Methods We studied incident continuous ambulatory peritoneal dialysis patients from Peking University Third Hospital. An ankle brachial index of less than 0.9 was used to diagnose PAD. Residual renal function (RRF) was determined as the mean of 24-hour urea and creatinine clearances (glomerular filtration rate). The Cox proportional hazards model was used to identify factors predicting loss of RRF. Results The study included 86 patients (age: 61 ± 14 years; men: 51%), 23 of whom had PAD at baseline. Mean follow-up was 19 months (median: 18 months; range: 6 – 30 months). In univariate analysis, baseline PAD, peritonitis during follow-up, inflammation (C-reactive protein), serum uric acid, CaxP, and serum phosphate were all significantly associated with a greater-than-50% decrease in RRF during follow-up. In multivariate analysis, only baseline PAD, CaxP, and peritonitis were independently associated with a decline in RRF. Conclusions Our study suggests that PAD may be a clinically important marker of CVD predicting the loss of RRF. It remains to be determined whether interventions aimed at decreasing PAD may also improve renal vascular status and thus slow the rate of RRF decline.
|
|
