A Terminal Event. Reply Lai, Andrew R; Gensler, Lianne S; McQuaid, Kenneth
The New England journal of medicine,
11/2019, Letnik:
381, Številka:
22
Journal Article
Machine learning methods are flexible prediction algorithms that may be more accurate than conventional regression. We compared the accuracy of different techniques for detecting clinical ...deterioration on the wards in a large, multicenter database.
Observational cohort study.
Five hospitals, from November 2008 until January 2013.
Hospitalized ward patients
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Demographic variables, laboratory values, and vital signs were utilized in a discrete-time survival analysis framework to predict the combined outcome of cardiac arrest, intensive care unit transfer, or death. Two logistic regression models (one using linear predictor terms and a second utilizing restricted cubic splines) were compared to several different machine learning methods. The models were derived in the first 60% of the data by date and then validated in the next 40%. For model derivation, each event time window was matched to a non-event window. All models were compared to each other and to the Modified Early Warning score, a commonly cited early warning score, using the area under the receiver operating characteristic curve (AUC). A total of 269,999 patients were admitted, and 424 cardiac arrests, 13,188 intensive care unit transfers, and 2,840 deaths occurred in the study. In the validation dataset, the random forest model was the most accurate model (AUC, 0.80 95% CI, 0.80-0.80). The logistic regression model with spline predictors was more accurate than the model utilizing linear predictors (AUC, 0.77 vs 0.74; p < 0.01), and all models were more accurate than the MEWS (AUC, 0.70 95% CI, 0.70-0.70).
In this multicenter study, we found that several machine learning methods more accurately predicted clinical deterioration than logistic regression. Use of detection algorithms derived from these techniques may result in improved identification of critically ill patients on the wards.
Prediction models are developed to aid health care providers in estimating the probability or risk that a specific disease or condition is present (diagnostic models) or that a specific event will ...occur in the future (prognostic models), to inform their decision making. However, the overwhelming evidence shows that the quality of reporting of prediction model studies is poor. Only with full and clear reporting of information on all aspects of a prediction model can risk of bias and potential usefulness of prediction models be adequately assessed. The Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) Initiative developed a set of recommendations for the reporting of studies developing, validating, or updating a prediction model, whether for diagnostic or prognostic purposes. This article describes how the TRIPOD Statement was developed. An extensive list of items based on a review of the literature was created, which was reduced after a Web-based survey and revised during a 3-day meeting in June 2011 with methodologists, health care professionals, and journal editors. The list was refined during several meetings of the steering group and in e-mail discussions with the wider group of TRIPOD contributors. The resulting TRIPOD Statement is a checklist of 22 items, deemed essential for transparent reporting of a prediction model study. The TRIPOD Statement aims to improve the transparency of the reporting of a prediction model study regardless of the study methods used. The TRIPOD Statement is best used in conjunction with the TRIPOD explanation and elaboration document. To aid the editorial process and readers of prediction model studies, it is recommended that authors include a completed checklist in their submission (also available at www.tripod-statement.org).
To investigate the usefulness of multistate models (MSM) for determining colorectal cancer (CRC) recurrence rate, to analyse the effect of different factors on tumour recurrence and death, and to ...assess the impact of recurrence for CRC prognosis.
Observational follow-up study of incident CRC cases disease-free after curative resection in 2006–2013 (n = 994). Recurrence and mortality were analyzed with MSM, as well as covariate effects on transition probabilities.
Cumulative incidence of recurrence at 60 months was 13.7%. Five years after surgery, 70.3% of patients were alive and recurrence-free, and 8.4% were alive after recurrence.
Recurrence has a negative impact on prognosis, with 5-year CRC-related mortality increasing from 3.8% for those who are recurrence-free 1-year after surgery to 33.6% for those with a recurrence.
Advanced stage increases recurrence risk (HR = 1.53) and CRC-related mortality after recurrence (HR = 2.35). CRC-related death was associated with age in recurrence-free patients, and with comorbidity after recurrence.
As expected, age≥75 years was a risk factor for non-CRC-related death with (HR = 7.76) or without recurrence (HR = 4.26), while its effect on recurrence risk was not demonstrated.
MSM allows detailed analysis of recurrence and mortality in CRC. Recurrence has a negative impact on prognosis. Advanced stage was a determining factor for recurrence and CRC-death after recurrence.
The COVID-19 pandemic is having profound mental health consequences for many people. Concerns have been expressed that, at their most extreme, these consequences could manifest as increased suicide ...rates. We aimed to assess the early effect of the COVID-19 pandemic on suicide rates around the world.
We sourced real-time suicide data from countries or areas within countries through a systematic internet search and recourse to our networks and the published literature. Between Sept 1 and Nov 1, 2020, we searched the official websites of these countries' ministries of health, police agencies, and government-run statistics agencies or equivalents, using the translated search terms "suicide" and "cause of death", before broadening the search in an attempt to identify data through other public sources. Data were included from a given country or area if they came from an official government source and were available at a monthly level from at least Jan 1, 2019, to July 31, 2020. Our internet searches were restricted to countries with more than 3 million residents for pragmatic reasons, but we relaxed this rule for countries identified through the literature and our networks. Areas within countries could also be included with populations of less than 3 million. We used an interrupted time-series analysis to model the trend in monthly suicides before COVID-19 (from at least Jan 1, 2019, to March 31, 2020) in each country or area within a country, comparing the expected number of suicides derived from the model with the observed number of suicides in the early months of the pandemic (from April 1 to July 31, 2020, in the primary analysis).
We sourced data from 21 countries (16 high-income and five upper-middle-income countries), including whole-country data in ten countries and data for various areas in 11 countries). Rate ratios (RRs) and 95% CIs based on the observed versus expected numbers of suicides showed no evidence of a significant increase in risk of suicide since the pandemic began in any country or area. There was statistical evidence of a decrease in suicide compared with the expected number in 12 countries or areas: New South Wales, Australia (RR 0·81 95% CI 0·72-0·91); Alberta, Canada (0·80 0·68-0·93); British Columbia, Canada (0·76 0·66-0·87); Chile (0·85 0·78-0·94); Leipzig, Germany (0·49 0·32-0·74); Japan (0·94 0·91-0·96); New Zealand (0·79 0·68-0·91); South Korea (0·94 0·92-0·97); California, USA (0·90 0·85-0·95); Illinois (Cook County), USA (0·79 0·67-0·93); Texas (four counties), USA (0·82 0·68-0·98); and Ecuador (0·74 0·67-0·82).
This is the first study to examine suicides occurring in the context of the COVID-19 pandemic in multiple countries. In high-income and upper-middle-income countries, suicide numbers have remained largely unchanged or declined in the early months of the pandemic compared with the expected levels based on the pre-pandemic period. We need to remain vigilant and be poised to respond if the situation changes as the longer-term mental health and economic effects of the pandemic unfold.
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