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Prediction models for diagnosis and prognosis of covid-19: systematic review and critical appraisal

BMJ 2020; 369 doi: https://doi.org/10.1136/bmj.m1328 (Published 07 April 2020) Cite this as: BMJ 2020;369:m1328

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Prediction models for diagnosis and prognosis in covid-19

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  1. Laure Wynants, assistant professor1 2,
  2. Ben Van Calster, associate professor2 3,
  3. Gary S Collins, professor4 5,
  4. Richard D Riley, professor6,
  5. Georg Heinze, associate professor7,
  6. Ewoud Schuit, assistant professor8 9,
  7. Marc M J Bonten, professor8 10,
  8. Darren L Dahly, principal statistician11 12,
  9. Johanna A Damen, assistant professor8 9,
  10. Thomas P A Debray, assistant professor8 9,
  11. Valentijn M T de Jong, assistant professor8 9,
  12. Maarten De Vos, associate professor2 13,
  13. Paula Dhiman, research fellow4 5,
  14. Maria C Haller, medical doctor7 14,
  15. Michael O Harhay, assistant professor15 16,
  16. Liesbet Henckaerts, assistant professor17 18,
  17. Pauline Heus, assistant professor8 9,
  18. Michael Kammer, research associate7 19,
  19. Nina Kreuzberger, research associate20,
  20. Anna Lohmann, researcher in training21,
  21. Kim Luijken, doctoral candidate21,
  22. Jie Ma, medical statistician5,
  23. Glen P Martin, lecturer22,
  24. David J McLernon, senior research fellow23,
  25. Constanza L Andaur Navarro, doctoral student8 9,
  26. Johannes B Reitsma, associate professor8 9,
  27. Jamie C Sergeant, senior lecturer24 25,
  28. Chunhu Shi, research associate26,
  29. Nicole Skoetz, medical doctor19,
  30. Luc J M Smits, professor1,
  31. Kym I E Snell, lecturer6,
  32. Matthew Sperrin, senior lecturer27,
  33. René Spijker, information specialist8 9 28,
  34. Ewout W Steyerberg, professor3,
  35. Toshihiko Takada, assistant professor8,
  36. Ioanna Tzoulaki, assistant professor29 30,
  37. Sander M J van Kuijk, research fellow31,
  38. Bas C T van Bussel, medical doctor1 32,
  39. Iwan C C van der Horst, professor32,
  40. Florien S van Royen, research fellow8,
  41. Jan Y Verbakel, assistant professor33 34,
  42. Christine Wallisch, research fellow7 35 36,
  43. Jack Wilkinson, research fellow22,
  44. Robert Wolff, medical doctor37,
  45. Lotty Hooft, associate professor8 9,
  46. Karel G M Moons, professor8 9,
  47. Maarten van Smeden, assistant professor8
  1. 1Department of Epidemiology, CAPHRI Care and Public Health Research Institute, Maastricht University, Peter Debyeplein 1, 6229 HA Maastricht, Netherlands
  2. 2Department of Development and Regeneration, KU Leuven, Leuven, Belgium
  3. 3Department of Biomedical Data Sciences, Leiden University Medical Centre, Leiden, Netherlands
  4. 4Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Musculoskeletal Sciences, University of Oxford, Oxford, UK
  5. 5NIHR Oxford Biomedical Research Centre, John Radcliffe Hospital, Oxford, UK
  6. 6Centre for Prognosis Research, School of Primary, Community and Social Care, Keele University, Keele, UK
  7. 7Section for Clinical Biometrics, Centre for Medical Statistics, Informatics and Intelligent Systems, Medical University of Vienna, Vienna, Austria
  8. 8Julius Center for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
  9. 9Cochrane Netherlands, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
  10. 10Department of Medical Microbiology, University Medical Centre Utrecht, Utrecht, Netherlands
  11. 11HRB Clinical Research Facility, Cork, Ireland
  12. 12School of Public Health, University College Cork, Cork, Ireland
  13. 13Department of Electrical Engineering, ESAT Stadius, KU Leuven, Leuven, Belgium
  14. 14Ordensklinikum Linz, Hospital Elisabethinen, Department of Nephrology, Linz, Austria
  15. 15Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
  16. 16Palliative and Advanced Illness Research Center and Division of Pulmonary and Critical Care Medicine, Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
  17. 17Department of Microbiology, Immunology and Transplantation, KU Leuven-University of Leuven, Leuven, Belgium
  18. 18Department of General Internal Medicine, KU Leuven-University Hospitals Leuven, Leuven, Belgium
  19. 19Department of Nephrology, Medical University of Vienna, Vienna, Austria
  20. 20Evidence-Based Oncology, Department I of Internal Medicine and Centre for Integrated Oncology Aachen Bonn Cologne Dusseldorf, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
  21. 21Department of Clinical Epidemiology, Leiden University Medical Centre, Leiden, Netherlands
  22. 22Division of Informatics, Imaging and Data Science, Faculty of Biology, Medicine and Health, Manchester Academic Health Science Centre, University of Manchester, Manchester, UK
  23. 23Institute of Applied Health Sciences, University of Aberdeen, Aberdeen, UK
  24. 24Centre for Biostatistics, University of Manchester, Manchester Academic Health Science Centre, Manchester, UK
  25. 25Centre for Epidemiology Versus Arthritis, Centre for Musculoskeletal Research, University of Manchester, Manchester Academic Health Science Centre, Manchester, UK
  26. 26Division of Nursing, Midwifery and Social Work, School of Health Sciences, University of Manchester, Manchester, UK
  27. 27Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK
  28. 28Amsterdam UMC, University of Amsterdam, Amsterdam Public Health, Medical Library, Netherlands
  29. 29Department of Epidemiology and Biostatistics, Imperial College London School of Public Health, London, UK
  30. 30Department of Hygiene and Epidemiology, University of Ioannina Medical School, Ioannina, Greece
  31. 31Department of Clinical Epidemiology and Medical Technology Assessment, Maastricht University Medical Centre+, Maastricht, Netherlands
  32. 32Department of Intensive Care, Maastricht University Medical Centre+, Maastricht University, Maastricht, Netherlands
  33. 33EPI-Centre, Department of Public Health and Primary Care, KU Leuven, Leuven, Belgium
  34. 34Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
  35. 35Charité Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, Berlin, Germany
  36. 36Berlin Institute of Health, Berlin, Germany
  37. 37Kleijnen Systematic Reviews, York, UK
  1. Correspondence to: L Wynants laure.wynants{at}maastrichtuniversity.nl
  • Accepted 31 March 2020
  • Final version accepted 12 January 2021

Abstract

Objective To review and appraise the validity and usefulness of published and preprint reports of prediction models for diagnosing coronavirus disease 2019 (covid-19) in patients with suspected infection, for prognosis of patients with covid-19, and for detecting people in the general population at increased risk of covid-19 infection or being admitted to hospital with the disease.

Design Living systematic review and critical appraisal by the COVID-PRECISE (Precise Risk Estimation to optimise covid-19 Care for Infected or Suspected patients in diverse sEttings) group.

Data sources PubMed and Embase through Ovid, up to 1 July 2020, supplemented with arXiv, medRxiv, and bioRxiv up to 5 May 2020.

Study selection Studies that developed or validated a multivariable covid-19 related prediction model.

Data extraction At least two authors independently extracted data using the CHARMS (critical appraisal and data extraction for systematic reviews of prediction modelling studies) checklist; risk of bias was assessed using PROBAST (prediction model risk of bias assessment tool).

Results 37 421 titles were screened, and 169 studies describing 232 prediction models were included. The review identified seven models for identifying people at risk in the general population; 118 diagnostic models for detecting covid-19 (75 were based on medical imaging, 10 to diagnose disease severity); and 107 prognostic models for predicting mortality risk, progression to severe disease, intensive care unit admission, ventilation, intubation, or length of hospital stay. The most frequent types of predictors included in the covid-19 prediction models are vital signs, age, comorbidities, and image features. Flu-like symptoms are frequently predictive in diagnostic models, while sex, C reactive protein, and lymphocyte counts are frequent prognostic factors. Reported C index estimates from the strongest form of validation available per model ranged from 0.71 to 0.99 in prediction models for the general population, from 0.65 to more than 0.99 in diagnostic models, and from 0.54 to 0.99 in prognostic models. All models were rated at high or unclear risk of bias, mostly because of non-representative selection of control patients, exclusion of patients who had not experienced the event of interest by the end of the study, high risk of model overfitting, and unclear reporting. Many models did not include a description of the target population (n=27, 12%) or care setting (n=75, 32%), and only 11 (5%) were externally validated by a calibration plot. The Jehi diagnostic model and the 4C mortality score were identified as promising models.

Conclusion Prediction models for covid-19 are quickly entering the academic literature to support medical decision making at a time when they are urgently needed. This review indicates that almost all pubished prediction models are poorly reported, and at high risk of bias such that their reported predictive performance is probably optimistic. However, we have identified two (one diagnostic and one prognostic) promising models that should soon be validated in multiple cohorts, preferably through collaborative efforts and data sharing to also allow an investigation of the stability and heterogeneity in their performance across populations and settings. Details on all reviewed models are publicly available at https://www.covprecise.org/. Methodological guidance as provided in this paper should be followed because unreliable predictions could cause more harm than benefit in guiding clinical decisions. Finally, prediction model authors should adhere to the TRIPOD (transparent reporting of a multivariable prediction model for individual prognosis or diagnosis) reporting guideline.

Systematic review registration Protocol https://osf.io/ehc47/, registration https://osf.io/wy245.

Readers’ note This article is a living systematic review that will be updated to reflect emerging evidence. Updates may occur for up to two years from the date of original publication. This version is update 3 of the original article published on 7 April 2020 (BMJ 2020;369:m1328). Previous updates can be found as data supplements (https://www.bmj.com/content/369/bmj.m1328/related#datasupp). When citing this paper please consider adding the update number and date of access for clarity.

Introduction

The novel coronavirus disease 2019 (covid-19) presents an important and urgent threat to global health. Since the outbreak in early December 2019 in the Hubei province of the People’s Republic of China, the number of patients confirmed to have the disease has exceeded 47 million as the disease spread globally, and the number of people infected is probably much higher. More than 1.2 million people have died from covid-19 (up to 3 November 2020).1 Despite public health responses aimed at containing the disease and delaying the spread, several countries have been confronted with a critical care crisis, and more countries could follow.234 Outbreaks lead to important increases in the demand for hospital beds and shortage of medical equipment, while medical staff themselves can also become infected. Several regions have had or are experiencing second waves, and despite improvements in testing and tracing, several regions are again facing the limits of their test capacity, hospital resources and healthcare staff.56

To mitigate the burden on the healthcare system, while also providing the best possible care for patients, efficient diagnosis and information on the prognosis of the disease are needed. Prediction models that combine several variables or features to estimate the risk of people being infected or experiencing a poor outcome from the infection could assist medical staff in triaging patients when allocating limited healthcare resources. Models ranging from rule based scoring systems to advanced machine learning models (deep learning) have been proposed and published in response to a call to share relevant covid-19 research findings rapidly and openly to inform the public health response and help save lives.7

We aimed to systematically review and critically appraise all currently available prediction models for covid-19, in particular models to predict the risk of covid-19 infection or being admitted to hospital with the disease, models to predict the presence of covid-19 in patients with suspected infection, and models to predict the prognosis or course of infection in patients with covid-19. We included model development and external validation studies. This living systematic review, with periodic updates, is being conducted by the international COVID-PRECISE (Precise Risk Estimation to optimise covid-19 Care for Infected or Suspected patients in diverse sEttings; https://www.covprecise.org/) group in collaboration with the Cochrane Prognosis Methods Group.

Methods

We searched the publicly available, continuously updated publication list of the covid-19 living systematic review.8 We validated whether the list is fit for purpose (online supplementary material) and further supplemented it with studies on covid-19 retrieved from arXiv. The online supplementary material presents the search strings. We included studies if they developed or validated a multivariable model or scoring system, based on individual participant level data, to predict any covid-19 related outcome. These models included three types of prediction models: diagnostic models to predict the presence or severity of covid-19 in patients with suspected infection; prognostic models to predict the course of infection in patients with covid-19; and prediction models to identify people in the general population at risk of covid-19 infection or at risk of being admitted to hospital with the disease.

We searched the database repeatedly up to 1 July 2020 (supplementary table 1). As of the third update (search date 1 July), we only include peer reviewed articles (indexed in PubMed and Embase through Ovid). Preprints (from bioRxiv, medRxiv, and arXiv) that were already included in previous updates of the systematic review remain included in the analysis. Reassessment takes place after publication of a preprint in a peer reviewed journal. No restrictions were made on the setting (eg, inpatients, outpatients, or general population), prediction horizon (how far ahead the model predicts), included predictors, or outcomes. Epidemiological studies that aimed to model disease transmission or fatality rates, diagnostic test accuracy, and predictor finding studies were excluded. We focus on studies published in English. Starting with the second update, retrieved records were initially screened by a text analysis tool developed using artificial intelligence to prioritise sensitivity (supplementary material). Titles, abstracts, and full texts were screened for eligibility in duplicate by independent reviewers (pairs from LW, BVC, MvS) using EPPI-Reviewer,9 and discrepancies were resolved through discussion.

Data extraction of included articles was done by two independent reviewers (from LW, BVC, GSC, TPAD, MCH, GH, KGMM, RDR, ES, LJMS, EWS, KIES, CW, AL, JM, TT, JAAD, KL, JBR, LH, CS, MS, MCH, NS, NK, SMJvK, JCS, PD, CLAN, RW, GPM, IT, JYV, DLD, JW, FSvR, PH, VMTdJ, BCTvB, ICCvdH, DJM, MK, and MvS). Reviewers used a standardised data extraction form based on the CHARMS (critical appraisal and data extraction for systematic reviews of prediction modelling studies) checklist10 and PROBAST (prediction model risk of bias assessment tool; www.probast.org) for assessing the reported prediction models.11 We sought to extract each model’s predictive performance by using whatever measures were presented. These measures included any summaries of discrimination (the extent to which predicted risks discriminate between participants with and without the outcome), and calibration (the extent to which predicted risks correspond to observed risks) as recommended in the TRIPOD (transparent reporting of a multivariable prediction model for individual prognosis or diagnosis; www.tripod-statement.org) statement.12 Discrimination is often quantified by the C index (C index=1 if the model discriminates perfectly; C index=0.5 if discrimination is no better than chance). Calibration is often quantified by the calibration intercept (which is zero when the risks are not systematically overestimated or underestimated) and calibration slope (which is one if the predicted risks are not too extreme or too moderate).13 We focused on performance statistics as estimated from the strongest available form of validation (in order of strength: external (evaluation in an independent database), internal (bootstrap validation, cross validation, random training test splits, temporal splits), apparent (evaluation by using exactly the same data used for development)). Any discrepancies in data extraction were discussed between reviewers, and remaining conflicts were resolved by LW or MvS. The online supplementary material provides details on data extraction. Some studies investigated multiple models and some models were investigated in multiple studies (that is, in external validation studies). The unit of analysis was a model within a study, unless stated otherwise. We considered aspects of PRISMA (preferred reporting items for systematic reviews and meta-analyses)14 and TRIPOD12 in reporting our study. Details on all reviewed studies and prediction models are publicly available at https://www.covprecise.org/.

Patient and public involvement

It was not possible to involve patients or the public in the design, conduct, or reporting of our research. A lay summary of the project’s aims is available on https://www.covprecise.org/project/. The study protocol and preliminary results are publicly available on https://osf.io/ehc47/, medRxiv and https://www.covprecise.org/living-review/.

Results

We retrieved 37 412 titles through our systematic search (of which 23 203 were included in the present update; supplementary table 1, fig 1). We included a further nine studies that were publicly available but were not detected by our search. Of 37 421 titles, 444 studies were retained for abstract and full text screening (of which 169 are included in the present update). One hundred sixty nine studies describing 232 prediction models met the inclusion criteria (of which 62 studies and 87 models added since the present update, supplementary table 1).15161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960616263646566676869707172737475767778798081828384858687888990919293949596979899100101102103104105106107108109110111112113114115116117118119120121122123124125126127128129130131132133134135136137138139140141142143144145146147148149150151152153154155156157158159160161162163164165166167168169170171172173174175176177178179180181182183 These studies were selected for data extraction and critical appraisal. The unit of analysis was the model within a study: of these 232 models, 208 were unique, newly developed models for covid-19. The remaining 24 analyses were external validations of existing models (in a study other than the model development study). Some models were validated more than once (in different studies, as described below). Many models are publicly available (box 1). A database with the description of each model and its risk of bias assessment can be found on https://www.covprecise.org/.

Fig 1
Fig 1

PRISMA (preferred reporting items for systematic reviews and meta-analyses) flowchart of study inclusions and exclusions

Box 1

Availability of models in format for use in clinical practice

Two hundred and eight unique models were developed in the included studies. Thirty (14%) of these models were presented as a model equation including intercept and regression coefficients. Eight (4%) models were only partially presented (eg, intercept or baseline hazard were missing). The remaining did not provide the underlying model equation.

Seventy two models (35%) are available as a tool for use in clinical practice (in addition to or instead of a published equation). Twenty seven models were presented as a web calculator (13%), 12 as a sum score (6%), 11 as a nomogram (5%), 8 as a software object (4%), 5 as a decision tree or set of predictions for subgroups (2%), 3 as a chart score (1%), and 6 in other usable formats (3%).

All these presentation formats make predictions readily available for use in the clinic. However, because all models were at high or uncertain risk of bias, we do not recommend their routine use before they are externally validated, ideally by independent investigators.

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Primary datasets

One hundred seventy four (75%) models used data from a single country (table 1), 42 (18%) models used international data, and for 16 (7%) models it was unclear how many (and which) countries contributed data. Two (1%) models used simulated data and 12 (5%) used proxy data to estimate covid-19 related risks (eg, Medicare claims data from 2015 to 2016). Most models were intended for use in confirmed covid-19 cases (47%) and a hospital setting (51%). The average patient age ranged from 39 to 71 years, and the proportion of men ranged from 35% to 75%, although this information was often not reported. One study developed a prediction model for use in paediatric patients.27

Table 1

Characteristics of reviewed prediction models for diagnosis and prognosis of coronavirus disease 2019 (covid-19)

View this table:

Based on the studies that reported study dates, data were collected from December 2019 to June 2020. Some centres provided data to multiple studies and several studies used open Github184 or Kaggle185 data repositories (version or date of access often unspecified), and so it was unclear how much these datasets overlapped across our identified studies.

Among the diagnostic model studies, the reported prevalence of covid-19 varied between 7% and 71% (if a cross sectional or cohort design was used). Because 75 diagnostic studies used either case-control sampling or an unclear method of data collection, the prevalence in these diagnostic studies might not be representative of their target population.

Among the studies that developed prognostic models to predict mortality risk in people with confirmed or suspected infection, the percentage of deaths ranged from 1% to 52%. This wide variation is partly because of substantial sampling bias caused by studies excluding participants who still had the disease at the end of the study period (that is, they had neither recovered nor died). Additionally, length of follow-up varied between studies (but was often not reported), and there is likely to be local and temporal variation in how people were diagnosed as having covid-19 or were admitted to the hospital (and therefore recruited for the studies).

Models to predict risk of covid-19 in the general population

We identified seven models that predicted risk of covid-19 in the general population. Three models from one study used hospital admission for non-tuberculosis pneumonia, influenza, acute bronchitis, or upper respiratory tract infections as proxy outcomes in a dataset without any patients with covid-19.16 Among the predictors were age, sex, previous hospital admission, comorbidities, and social determinants of health. The study reported C indices of 0.73, 0.81, and 0.81. A fourth model used deep learning on thermal videos from the faces of people wearing facemasks to determine abnormal breathing (not covid related) with a reported sensitivity of 80%.92 A fifth model used demographics, symptoms, and contact history in a mobile app to assist general practitioners in collecting data and to risk-stratify patients. It was contrasted with two further models that included additional blood values and blood values plus computed tomography (CT) images. The authors reported a C index of 0.71 with demographics only, which rose to 0.97 and 0.99 as blood values and imaging characteristics were added.151 Calibration was not assessed in any of the general population models.

Diagnostic models to detect covid-19 in patients with suspected infection

We identified 33 multivariable models to distinguish between patients with and without covid-19. Most models targeted patients with suspected covid-19. Reported C index values ranged between 0.65 and 0.99. Calibration was assessed for seven models using calibration plots (including two at external validation), with mixed results. The most frequently included predictors (≥10 times) were vital signs (eg, temperature, heart rate, respiratory rate, oxygen saturation, blood pressure), flu-like signs and symptoms (eg, shiver, fatigue), age, electrolytes, image features (eg, pneumonia signs on CT scan), contact with individuals with confirmed covid-19, lymphocyte count, neutrophil count, cough or sputum, sex, leukocytes, liver enzymes, and red cell distribution width.

Ten studies aimed to diagnose severe disease in patients with covid-19: nine in adults with reported C indices between value of 0.80 and 0.99, and one in children that reported perfect classification of severe disease.27 Calibration was not assessed in any of the models. Predictors of severe covid-19 used more than once were comorbidities, liver enzymes, C reactive protein, imaging features, lymphocyte count, and neutrophil count.

Seventy five prediction models were proposed to support the diagnosis of covid-19 or covid-19 pneumonia (and some also to monitor progression) based on images. Most studies used CT images or chest radiographs. Others used spectrograms of cough sounds55 and lung ultrasound.75 The predictive performance varied considerably, with reported C index values ranging from 0.70 to more than 0.99. Only one model based on imaging was evaluated by use of a calibration plot, and it appeared to be well calibrated at external validation.186

Prognostic models for patients with diagnosis of covid-19

We identified 107 prognostic models for patients with a diagnosis of covid-19. The intended use of these models (that is, when to use them, and for whom) was often not clearly described. Prediction horizons varied between one and 37 days, but were often unspecified.

Of these models, 39 estimated mortality risk and 28 aimed to predict progression to a severe or critical disease. The remaining studies used other outcomes (single or as part of a composite) including recovery, length of hospital stay, intensive care unit admission, intubation, (duration of) mechanical ventilation, acute respiratory distress syndrome, cardiac injury and thrombotic complication. One study used data from 2015 to 2019 to predict mortality and prolonged assisted mechanical ventilation (as a non-covid-19 proxy outcome).115 The most frequently used categories of prognostic factors (for any outcome, included at least 20 times) included age, comorbidities, vital signs, image features, sex, lymphocyte count, and C reactive protein.

Studies that predicted mortality reported C indices between 0.68 and 0.98. Four studies also presented calibration plots (including at external validation for three models), all indicating miscalibration1569118 or showing plots for integer scores without clearly explaining how these were translated into predicted risks.143 The studies that developed models to predict progression to a severe or critical disease reported C indices between 0.58 and 0.99. Five of these models also were evaluated by calibration plots, two of them at external validation. Even though calibration appeared good, plots were constructed in an unclear way.85121 Reported C indices for other outcomes varied between 0.54 (admission to intensive care) and 0.99 (severe symptoms three days after admission), and five models had calibration plots (of which three at external validation), with mixed results.

Risk of bias

All models were at high (n=226, 97%) or unclear (n=6, 3%) risk of bias according to assessment with PROBAST, which suggests that their predictive performance when used in practice is probably lower than that reported (fig 2). Therefore, we have cause for concern that the predictions of the proposed models are unreliable when used in other people. Figure 2 and box 2 gives details on common causes for risk of bias for each type of model.

Fig 2
Fig 2

PROBAST (prediction model risk of bias assessment tool) risk of bias for all included models combined (n=232) and broken down per type of model

Box 2

Common causes of risk of bias in the reported prediction models

Models to predict coronavirus disease 2019 (covid-19) risk in general population

All of these models had unclear or high risk of bias for the participant, outcome, and analysis domain. All were based on proxy outcomes to predict covid-19 related risks, such as presence of or hospital admission due to severe respiratory disease, in the absence of data of patients with covid-19.1692151

Diagnostic models

Ten models (30%) used inappropriate data sources (eg, due to a non-nested case-control design), nine (27%) used inappropriate inclusion or exclusion criteria such that the study data was not representative of the target population, and eight (24%) selected controls that were not representative of the target population for a diagnostic model (eg, controls for a screening model had viral pneumonia). Other frequent problems were dichotomisation of predictors (nine models, 27%), and tests used to determine the outcome (eight models, 24%) or predictor definitions or measurement procedures (seven models, 21%) that varied between participants.

Diagnostic models based for severity classification

Two models (20%) used predictor data that was assessed while the severity (the outcome) was known. Other concerns include non-standard or lack of a prespecified outcome definition (two models, 20%), predictor measurements (eg, fever) being part of the outcome definition (two models, 20%) and outcomes being assessed with knowledge of predictor measurements (two models, 20%).

Diagnostic models based on medical imaging

Generally, studies did not clearly report which patients had imaging during clinical routine. Fifty five (73%) used an inappropriate or unclear study design to collect data (eg, a non-nested case-control). It was often unclear (39 models, 52%) whether the selection of controls was made from the target population (that is, patients with suspected covid-19). Outcome definitions were often not defined or determined in the same way in all participants (18 models, 24%). Diagnostic model studies that used medical images as predictors were all scored as unclear on the predictor domain. These publications often lacked clear information on the preprocessing steps (eg, cropping of images). Moreover, complex machine learning algorithms transform images into predictors in a complex way, which makes it challenging to fully apply the PROBAST predictors section for such imaging studies. However, a more favourable assessment of the predictor domain does not lead to better overall judgment regarding risk of bias for the included models. Careful description of model specification and subsequent estimation were frequently lacking, challenging the transparency and reproducibility of the models. Studies used different deep learning architectures, some were established and others specifically designed, without benchmarking the used architecture against others.

Prognostic models

Dichotomisation of predictors was a frequent concern (22 models, 21%). Other problems include inappropriate inclusions or exclusions of study participants (18 models, 17%). Study participants were often excluded because they did not develop the outcome at the end of the study period but were still in follow-up (that is, they were in hospital but had not recovered or died), yielding a selected study sample (12 models, 11%). Additionally, many models (16 models, 15%) did not account for censoring or competing risks.

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Ninety eight models (42%) had a high risk of bias for the participants domain, which indicates that the participants enrolled in the studies might not be representative of the models’ targeted populations. Unclear reporting on the inclusion of participants led to an unclear risk of bias assessment in 58 models (25%), and 76 (33%) had a low risk of bias for the participants domain. Fifteen models (6%) had a high risk of bias for the predictor domain, which indicates that predictors were not available at the models’ intended time of use, not clearly defined, or influenced by the outcome measurement. One hundred and thirty five (58%) models were rated unclear and 82 (35%) rated at low risk of bias for the predictor domain. Most studies used outcomes that are easy to assess (eg, death, presence of covid-19 by laboratory confirmation), and hence 95 (41%) were rated at low risk of bias. Nonetheless, there was cause for concern about bias induced by the outcome measurement in 50 models (22%), for example, due to the use of subjective or proxy outcomes (eg, non-covid-19 severe respiratory infections). Eighty seven models (38%) had an unclear risk of bias due to opaque or ambiguous reporting. Two hundred and eighteen (94%) models were at high risk of bias for the analysis domain. The reporting was insufficiently clear to assess risk of bias in the analysis in 13 studies (6%). Only one model had a low risk of bias for the analysis domain (<1%). Twenty nine (13%) models had low risk of bias on all domains except analysis, indicating adequate data collection and study design, but issues that could have been avoided by conducting a better statistical analysis. Many studies had small to modest sample sizes (table 1), which led to an increased risk of overfitting, particularly if complex modelling strategies were used. In addition, 50 models (22%) were neither internally nor externally validated. Performance statistics calculated on the development data from these models are likely optimistic. Calibration was only assessed for 22 models using calibration plots (10%), of which 11 on external validation data.

We found two models that were generally of good quality, built on large datasets, and had been rated low risk of bias on most domains but with an overall rating of unclear risk of bias, owing to unclear details on one signalling question within the analysis domain (table 2 provides a summary). Jehi and colleagues presented findings from developing a diagnostic model, however, there was substantial missing data and it remains unclear whether the use of median imputation influenced results, and there are unexplained discrepancies between the online calculator, nomogram, and published logistic regression model.141 Hence, the calculator should not be used without further validation. Knight and colleagues developed a prognostic model for in-hospital mortality, however, continuous predictors were dichotomised, which reduces granularity of predicted risks (even though the model had a C index comparable with that of a generalised additive model).143 The model was also converted into an sum score, but it was unclear how the scores were translated to the predicted mortality risks that were used to evaluate calibration.

Table 2

Prediction models with unclear risk of bias overall and large development samples

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External validation

Forty six models were developed and externally validated in the same study (in an independent dataset, excluding random training test splits and temporal splits). In addition, 24 external validations of models were developed for covid-19 or before the covid-19 pandemic in separate studies. However, none of the external validations was scored as low risk of bias, three were rated as unclear risk of bias, and 67 were rated as high risk of bias. One common concern is that datasets used for the external validation were likely not representative of the target population (eg, patients not being recruited consecutively, use of an inappropriate study design, use of unrepresentative controls, exclusion of patients still in follow-up). Consequently, predictive performance could differ if the models are applied in the targeted population. Moreover, only 15 (21%) external validations had 100 or more events, which is the recommended minumum.187188 Only 11 (16%) external validations presented a calibration plot.

Table 3 shows the results of external validations that had at most an unclear risk of bias and at least 100 events in the external validation set. The model by Jehi et al has been discussed above.141 Luo and colleagues performed a validation of the CURB-65 score, originally developed to predict mortality of community acquired pneumonia, to assess its abilty to predict in-hospital mortality in patients with confirmed covid-19. This validation was conducted in a large retrospective cohort of patients admitted to two Chinese designated hospitals to treat patients with pneumonia from SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2).155 It was unclear whether all consecutive patients were included (although this is likely given the retrospective design), no calibration plot was used because the score gives an integer as output rather than estimates risks, and the score uses dichotomised predictors. Overall, the external validation by Luo et al was performed well. Studies that validated CURB-65 in patients with covid-19 obtained C indexes of 0.58, 0.74, 0.75, 0.84, and 0.88.130148155164189 These observed differences might be due to differences in risk of bias (all except Luo et al were rated high risk of bias), heterogeneity in study populations (South Korea, China, Turkey, and the United States), outcome definitions (progression to severe covid-19 v mortality), and sampling variability (number of events were 36, 55, 131, 201, and unclear).

Table 3

External validations with unclear risk of bias and large validation samples

View this table:

Discussion

In this systematic review of prediction models related to the covid-19 pandemic, we identified and critically appraised 232 models described in 169 studies. These prediction models can be divided into three categories: models for the general population to predict the risk of having covid-19 or being admitted to hospital for covid-19; models to support the diagnosis of covid-19 in patients with suspected infection; and models to support the prognostication of patients with covid-19. All models reported moderate to excellent predictive performance, but all were appraised to have high or uncertain risk of bias owing to a combination of poor reporting and poor methodological conduct for participant selection, predictor description, and statistical methods used. Models were developed on data from different countries, but the majority used data from a single country. Often, the available sample sizes and number of events for the outcomes of interest were limited. This problem is well known when building prediction models and increases the risk of overfitting the model.190 A high risk of bias implies that the performance of these models in new samples will probably be worse than that reported by the researchers. Therefore, the estimated C indices, often close to 1 and indicating near perfect discrimination, are probably optimistic. The majority of studies developed new models specifically for covid-19, but only 46 carried out an external validation, and calibration was rarely assessed. We cannot yet recommend any of the identified prediction models for widespread use in clinical practice, although a few diagnostic and prognostic models originated from studies that were clearly of better quality. We suggest that these models should be further validated in other data sets, and ideally by independent investigators.141143

Challenges and opportunities

The main aim of prediction models is to support medical decision making in individual patients. Therefore, it is vital to identify a target setting in which predictions serve a clinical need (eg, emergency department, intensive care unit, general practice, symptom monitoring app in the general population), and a representative dataset from that setting (preferably comprising consecutive patients) on which the prediction model can be developed and validated. This clinical setting and patient characteristics should be described in detail (including timing within the disease course, the severity of disease at the moment of prediction, and the comorbidity), so that readers and clinicians are able to understand if the proposed model could be suited for their population. Unfortunately, the studies included in our systematic review often lacked an adequate description of the target setting and study population, which leaves users of these models in doubt about the models’ applicability. Although we recognise that the earlier studies were done under severe time constraints, we recommend that any studies currently in preprint and all future studies should adhere to the TRIPOD reporting guideline12 to improve the description of their study population and guide their modelling choices. TRIPOD translations (eg, in Chinese and Japanese) are also available at https://www.tripod-statement.org.

A better description of the study population could also help us understand the observed variability in the reported outcomes across studies, such as covid-19 related mortality and covid-19 prevalence. The variability in mortality could be related to differences in included patients (eg, age, comorbidities) and interventions for covid-19. The variability in prevalence could in part be reflective of different diagnostic standards across studies.

Covid-19 prediction will often not present as a simple binary classification task. Complexities in the data should be handled appropriately. For example, a prediction horizon should be specified for prognostic outcomes (eg, 30 day mortality). If study participants have neither recovered nor died within that time period, their data should not be excluded from analysis, which some reviewed studies have done. Instead, an appropriate time to event analysis should be considered to allow for administrative censoring.13 Censoring for other reasons, for instance because of quick recovery and loss to follow-up of patients who are no longer at risk of death from covid-19, could necessitate analysis in a competing risk framework.191

We reviewed 75 studies that used only medical images to diagnose covid-19, covid-19 related pneumonia, or to assist in segmentation of lung images, the majority using advanced machine learning methodology. The predictive performance measures showed a high to almost perfect ability to identify covid-19, although these models and their evaluations also had a high risk of bias, notably because of poor reporting and an artificial mix of patients with and without covid-19. Currently, none of these models is recommended to be used in clinical practice. An independent systematic review and critical appraisal (using PROBAST12) of machine learning models for covid-19 using chest radiographs and CT scans came to the same conclusions, even though they focused on models that met a minimum requirement of study quality based on specialised quality metrics for the assessment of radiomics and deep-learning based diagnostic models in radiology.192

A prediction model applied in a new healthcare setting or country often produces predictions that are miscalibrated193 and might need to be updated before it can safely be applied in that new setting.13 This requires data from patients with covid-19 to be available from that system. Instead of developing and updating predictions in their local setting, individual participant data from multiple countries and healthcare systems might allow better understanding of the generalisability and implementation of prediction models across different settings and populations. This approach could greatly improve the applicability and robustness of prediction models in routine care.194195196197198

The evidence base for the development and validation of prediction models related to covid-19 will continue to increase over the coming months. To leverage the full potential of these evolutions, international and interdisciplinary collaboration in terms of data acquisition, model building and validation is crucial.

Study limitations

With new publications on covid-19 related prediction models rapidly entering the medical literature, this systematic review cannot be viewed as an up-to-date list of all currently available covid-19 related prediction models. Also, 80 of the studies we reviewed were only available as preprints. These studies might improve after peer review, when they enter the official medical literature; we will reassess these peer reviewed publications in future updates. We also found other prediction models that are currently being used in clinical practice without scientific publications,199 and web risk calculators launched for use while the scientific manuscript is still under review (and unavailable on request).200 These unpublished models naturally fall outside the scope of this review of the literature. As we have argued extensively elsewhere,201 transparent reporting that enables validation by independent researchers is key for predictive analytics, and clinical guidelines should only recommend publicly available and verifiable algorithms.

Implications for practice

All reviewed prediction models were found to have an unclear or high risk of bias, and evidence from independent external validations of the newly developed models is still scarce. However, the urgency of diagnostic and prognostic models to assist in quick and efficient triage of patients in the covid-19 pandemic might encourage clinicians and policymakers to prematurely implement prediction models without sufficient documentation and validation. Earlier studies have shown that models were of limited use in the context of a pandemic,202 and they could even cause more harm than good.203 Therefore, we cannot recommend any model for use in practice at this point.

The current oversupply of insufficiently validated models is not useful for clinical practice. Moreover, predictive performance estimates obtained from different populations, settings, and types of validation (internal v external) are not directly comparable. Future studies should focus on validating, comparing, improving, and updating promising available prediction models.13 The models by Knight and colleagues143 and Jehi and colleagues141 are good candidates for validation studies in other data. We advise Jehi and colleagues to make all model equations available for independent validation.141 Such external validations should assess not only discrimination, but also calibration and clinical utility (net benefit),193198203 in large datasets187188 collected using an appropriate study design. In addition, these models’ transportability to other countries or settings remains to be investigated. Owing to differences between healthcare systems (eg, Chinese and European) and over time in when patients are admitted to and discharged from hospital, as well as the testing criteria for patients with suspected covid-19, we anticipate most existing models will be miscalibrated, but researchers could attempt to update and adjust the model to the local setting.

Most reviewed models used data from a hospital setting, but few are available for primary care and the general population. Additional research is needed, including validation of any recently proposed models not yet included in the current update of the living review (eg, Clift et al204). The models reviewed to date predicted the covid-19 diagnosis or assess the risk of mortality or deterioration, whereas long term morbidity and functional outcomes remain understudied and could be a target outcome of interest in future studies developing prediction models.205206

When creating a new prediction model, we recommend building on previous literature and expert opinion to select predictors, rather than selecting predictors in a purely data driven way.13 This is especially important for datasets with limited sample size.207 Frequently used predictors included in multiple models identified by our review are vital signs, age, comorbidities, and image features, and these should be considered when appropriate. Flu-like symptoms should be considered in diagnostic models, and sex, C reactive protein, and lymphocyte counts could be considered as prognostic factors.

By pointing to the most important methodological challenges and issues in design and reporting of the currently available models, we hope to have provided a useful starting point for further studies, which should preferably validate and update existing ones. This living systematic review has been conducted in collaboration with the Cochrane Prognosis Methods Group. We will update this review and appraisal continuously to provide up-to-date information for healthcare decision makers and professionals as more international research emerges over time.

Conclusion

Several diagnostic and prognostic models for covid-19 are currently available and they all report moderate to excellent discrimination. However, these models are all at high or unclear risk of bias, mainly because of model overfitting, inappropriate model evaluation (eg, calibration ignored), use of inappropriate data sources and unclear reporting. Therefore, their performance estimates are probably optimistic and not representative for the target population. The COVID-PRECISE group does not recommend any of the current prediction models to be used in practice, but one diagnostic and one prognostic model originated from higher quality studies and should be (independently) validated in other datasets. For details of the reviewed models, see https://www.covprecise.org/. Future studies aimed at developing and validating diagnostic or prognostic models for covid-19 should explicitly describe the concerns raised and follow existing methodological guidance for prediction modeling studies, because unreliable predictions could cause more harm than benefit in guiding clinical decisions. Prediction model authors should adhere to the TRIPOD (transparent reporting of a multivariable prediction model for individual prognosis or diagnosis) reporting guideline. Finally, sharing data and expertise for the validation and updating of covid-19 related prediction models is urgently needed.

What is already known on this topic

  • The sharp recent increase in coronavirus disease 2019 (covid-19) incidence has put a strain on healthcare systems worldwide; an urgent need exists for efficient early detection of covid-19 in the general population, for diagnosis of covid-19 in patients with suspected disease, and for prognosis of covid-19 in patients with confirmed disease

  • Viral nucleic acid testing and chest computed tomography imaging are standard methods for diagnosing covid-19, but are time consuming

  • Earlier reports suggest that elderly patients, patients with comorbidities (chronic obstructive pulmonary disease, cardiovascular disease, hypertension), and patients presenting with dyspnoea are vulnerable to more severe morbidity and mortality after infection

What this study adds

  • Seven models identified patients at risk in the general population (using proxy outcomes for covid-19)

  • Thirty three diagnostic models were identified for detecting covid-19, in addition to 75 diagnostic models based on medical images, 10 diagnostic models for severity classification, and 107 prognostic models for predicting, among others, mortality risk, progression to severe disease

  • Proposed models are poorly reported and at high risk of bias, raising concern that their predictions could be unreliable when applied in daily practice

  • Two prediction models (one for diagnosis and one for prognosis) were identified as being of higher quality than others and efforts should be made to validate these in other datasets

Acknowledgments

We thank the authors who made their work available by posting it on public registries or sharing it confidentially. A preprint version of the study is publicly available on medRxiv.

Footnotes

  • Contributors: LW conceived the study. LW and MvS designed the study. LW, MvS, and BVC screened titles and abstracts for inclusion. LW, BVC, GSC, TPAD, MCH, GH, KGMM, RDR, ES, LJMS, EWS, KIES, CW, JAAD, PD, MCH, NK, AL, KL, JM, CLAN, JBR, JCS, CS, NS, MS, RS, TT, SMJvK, FSvR, LH, RW, GPM, IT, JYV, DLD, JW, FSvR, PH, VMTdJ, MK, ICCvdH, BCTvB, DJM, and MvS extracted and analysed data. MDV helped interpret the findings on deep learning studies and MMJB, LH, and MCH assisted in the interpretation from a clinical viewpoint. RS and FSvR offered technical and administrative support. LW and MvS wrote the first draft, which all authors revised for critical content. All authors approved the final manuscript. LW and MvS are the guarantors. The guarantors had full access to all the data in the study, take responsibility for the integrity of the data and the accuracy of the data analysis, and had final responsibility for the decision to submit for publication. The corresponding author attests that all listed authors meet authorship criteria and that no others meeting the criteria have been omitted.

  • Funding: LW, BVC, LH, and MDV acknowledge specific funding for this work from Internal Funds KU Leuven, KOOR, and the COVID-19 Fund. LW is a postdoctoral fellow of Research Foundation-Flanders (FWO) and receives support from ZonMw (grant 10430012010001). BVC received support from FWO (grant G0B4716N) and Internal Funds KU Leuven (grant C24/15/037). TPAD acknowledges financial support from the Netherlands Organisation for Health Research and Development (grant 91617050). VMTdJ was supported by the European Union Horizon 2020 Research and Innovation Programme under ReCoDID grant agreement 825746. KGMM and JAAD acknowledge financial support from Cochrane Collaboration (SMF 2018). KIES is funded by the National Institute for Health Research (NIHR) School for Primary Care Research. The views expressed are those of the author(s) and not necessarily those of the NHS, the NIHR, or the Department of Health and Social Care. GSC was supported by the NIHR Biomedical Research Centre, Oxford, and Cancer Research UK (programme grant C49297/A27294). JM was supported by the Cancer Research UK (programme grant C49297/A27294). PD was supported by the NIHR Biomedical Research Centre, Oxford. MOH is supported by the National Heart, Lung, and Blood Institute of the United States National Institutes of Health (grant R00 HL141678). ICCvDH and BCTvB received funding from Euregio Meuse-Rhine (grant Covid Data Platform (coDaP) interref EMR-187). The funders played no role in study design, data collection, data analysis, data interpretation, or reporting.

  • Competing interests: All authors have completed the ICMJE uniform disclosure form at www.icmje.org/coi_disclosure.pdf and declare: support from Internal Funds KU Leuven, KOOR, and the COVID-19 Fund for the submitted work; no competing interests with regards to the submitted work; LW discloses support from Research Foundation-Flanders; RDR reports personal fees as a statistics editor for The BMJ (since 2009), consultancy fees for Roche for giving meta-analysis teaching and advice in October 2018, and personal fees for delivering in-house training courses at Barts and the London School of Medicine and Dentistry, and the Universities of Aberdeen, Exeter, and Leeds, all outside the submitted work; MS coauthored the editorial on the original article.

  • Ethical approval: Not required.

  • Data sharing: The study protocol is available online at https://osf.io/ehc47/. Detailed extracted data on all included studies are available on https://www.covprecise.org/.

  • The lead authors affirm that the manuscript is an honest, accurate, and transparent account of the study being reported; that no important aspects of the study have been omitted; and that any discrepancies from the study as planned have been explained.

  • Dissemination to participants and related patient and public communities: The study protocol is available online at https://osf.io/ehc47/.

  • Provenance and peer review: Not commissioned; externally peer reviewed.

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References

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