Clinical prediction models for mortality in patients with covid-19: external validation and individual participant data meta-analysis

Abstract

Objective To externally validate various prognostic models and scoring rules for predicting short term mortality in patients admitted to hospital for covid-19. Design Two stage individual participant data meta-analysis. Setting Secondary and tertiary care. Participants 46 914 patients across 18 countries, admitted to a hospital with polymerase chain reaction confirmed covid-19 from November 2019 to April 2021. Data sources Multiple (clustered) cohorts in Brazil, Belgium, China, Czech Republic, Egypt, France, Iran, Israel, Italy, Mexico, Netherlands, Portugal, Russia, Saudi Arabia, Spain, Sweden, United Kingdom, and United States previously identified by a living systematic review of covid-19 prediction models published in The BMJ , and through PROSPERO, reference checking, and expert knowledge. Model selection and eligibility criteria Prognostic models identified by the living systematic review and through contacting experts. A priori models were excluded that had a high risk of bias in the participant domain of PROBAST (prediction model study risk of bias assessment tool) or for which the applicability was deemed poor. Methods Eight prognostic models with diverse predictors were identified and validated. A two stage individual participant data meta-analysis was performed of the estimated model concordance (C) statistic, calibration slope, calibration-in-the-large, and observed to expected ratio (O:E) across the included clusters. Main outcome measures 30 day mortality or in-hospital mortality. Results Datasets included 27 clusters from 18 different countries and contained data on 46 914patients. The pooled estimates ranged from 0.67 to 0.80 (C statistic), 0.22 to 1.22 (calibration slope), and 0.18 to 2.59 (O:E ratio) and were prone to substantial between study heterogeneity. The 4C Mortality Score by Knight et al (pooled C statistic 0.80, 95% confidence interval 0.75 to 0.84, 95% prediction interval 0.72 to 0.86) and clinical model by Wang et al (0.77, 0.73 to 0.80, 0.63 to 0.87) had the highest discriminative ability. On average, 29% fewer deaths were observed than predicted by the 4C Mortality Score (pooled O:E 0.71, 95% confidence interval 0.45 to 1.11, 95% prediction interval 0.21 to 2.39), 35% fewer than predicted by the Wang clinical model (0.65, 0.52 to 0.82, 0.23 to 1.89), and 4% fewer than predicted by Xie et al’s model (0.96, 0.59 to 1.55, 0.21 to 4.28). Conclusion The prognostic value of the included models varied greatly between the data sources. Although the Knight 4C Mortality Score and Wang clinical model appeared most promising, recalibration (intercept and slope updates) is needed before implementation in routine care.

Long covid and apheresis: a miracle cure sold on a hypothesis of hope

“…One of those hopes was that the covid pandemic was “over” in February (https://www.dailymail.co.uk/news/article-10493765/Boris-Johnson-declares-Covid-rules-MONTH.html),2 a political gambit to lift all restrictions that defied hard evidence and cold logic (doi:10.1136/bmj.o1, doi:10.1136/bmj.o1555, doi:10.1136/bmj-2021-069881). 345 SARS-CoV-2 never went away, even if its impact was dulled, and it is now most definitely back, sending people to hospital and intensive care (doi:10.1136/bmj.o1702). 6 Hospitals are reintroducing masking for staff (doi:10.1136/bmj.o1712),7 although the general public seems blissfully ignorant of the latest omicron variant.…”

COVID-19 individual participant data meta-analyses. Can there be too many? Results from a rapid systematic review

“…One IPD-MA assessed ICU mortality, [14] and another pregnancy-related mortality. [25] IPD-MAs that specified time-to-death, included: 14-day mortality (n=2), [27,36] 28-day mortality (n=2), [13,36] 30-day mortality (n=2), [12,27] and 60-day mortality (n=1). [13] Individual IPD-MAs focused on the clinical presentation of long COVID-19 [22] and COVID-19 in children; [23] time-to-recovery of smell or taste; [42] incidence of Guillain-Barre syndrome; [18] rate and spectrum of dermatological outcomes in COVID-19 patients; [17] viral load at of sample collection and its effect on the accuracy of RT-PCR; [20] and adverse birth outcomes and vertical transmission in pregnant women with COVID-19.…”

“…[14][15][16] Two IPD-MAs had case reports and case series. [17,18] One IPD-MA each was limited to case reports, [19] medical records, [12] and case series and longitudinal studies. [20] One IPD-MA included any study design; [21] two others included any study design other than case reports.…”

Lack of Correlation Between Soluble Angiotensin-Converting Enzyme 2 and Inflammatory Markers in Hospitalized COVID-19 Patients with Hypertension

“… 45 , 46 Yet; several studies suggested that rather than a single parameter, multiple markers should be used as predictors of severity and mortality in patients with COVID-19. 47 , 48 …”

“…45,46 Yet; several studies suggested that rather than a single parameter, multiple markers should be used as predictors of severity and mortality in patients with COVID-19. 47,48 Regardless of all the above findings, the incomplete knowledge of the association between COVID and ACE2 has led to debates concerning the use of RAAS inhibitors. Our study found no concern regarding the inflammatory markers; we even found a better ratio of IL-6/IL-10 in patients treated with RAAS inhibitors.…”